Search Results (123)

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

Infrared-visible image fusion quality is critical for nighttime perception in autonomous driving and surveillance but suffers severe degradation under extreme low-light conditions, including irreversible texture loss in visible images, thermal boundary diffusion artifacts, and overexposure under dynamic non-uniform illumination. To address these challenges, a Decomposition–Disentanglement–Dynamic Compensation framework, D3Fusion, is proposed. Firstly, a Retinex-inspired Decomposition Illumination Net (DIN) decomposes inputs into enhanced images and degradative illumination maps for joint low-light recovery. Secondly, an illumination-guided encoder and a multi-scale differential compensation decoder dynamically balance cross-modal features. Finally, a progressive three-stage training paradigm from illumination correction through feature disentanglement to adaptive fusion resolves optimization conflicts. Compared to State-of-the-Art methods, on the LLVIP, TNO, MSRS, and RoadScene datasets, D3Fusion achieves an average improvement of 1.59% in standard deviation (SD), 6.9% in spatial frequency (SF), 2.59% in edge intensity (EI), and 1.99% in visual information fidelity (VIF), demonstrating superior performance in extreme low-light scenarios. The framework effectively suppresses thermal diffusion artifacts while mitigating exposure imbalance, adaptively brightening scenes while preserving texture details in shadowed regions. This significantly improves fusion quality for nighttime images by enhancing salient information, establishing a robust solution for multimodal perception under illumination-critical conditions. Full article

Accurate State of Charge (SOC) estimation for retired power batteries remains a critical challenge due to their degraded electrochemical properties and heterogeneous aging mechanisms. Traditional methods relying solely on electrical parameters (e.g., voltage and current) exhibit significant errors, as aged batteries experience altered internal resistance, capacity fade, and uneven heat generation, which distort the relationship between electrical signals and actual SOC. To address these limitations, this study proposes a surface temperature-assisted SOC estimation method, leveraging the distinct thermal characteristics of retired batteries. By employing infrared thermal imaging, key temperature feature regions—the positive/negative tabs and central area—are identified, which exhibit strong correlations with SOC dynamics under varying operational conditions. A Gated Recurrent Unit (GRU) neural network is developed to integrate multi-region temperature data with electrical parameters, capturing spatial–temporal thermal–electrical interactions unique to retired batteries. The model is trained and validated using experimental data collected under constant current discharge conditions, demonstrating superior accuracy compared to conventional methods. Specifically, our method achieves 64.3–68.1% lower RMSE than traditional electrical-parameter-only approaches (V-I inputs) across 0.5 C–2 C discharge rates. Results show that the proposed method reduces SOC estimation errors compared to traditional voltage-based models, achieving RMSE values below 1.04 across all tested rates. This improvement stems from the model’s ability to decode localized heating patterns and their hysteresis effects, which are particularly pronounced in aged batteries. The method’s robustness under high-rate operations highlights its potential for enhancing the reliability of retired battery management systems in secondary applications such as energy storage. Full article

Background: Pain is a complex phenomenon characterized by unpleasant experiences with profound heterogeneity influenced by biological, psychological, and social factors. According to the National Health Interview Survey, 50.2 million U.S. adults (20.5%) experience pain on most days, with the annual cost of prescription medication for pain reaching approximately USD 17.8 billion. Theranostic pain nanomedicine therefore emerges as an attractive analgesic strategy with the potential for increased efficacy, reduced side-effects, and treatment personalization. Theranostic nanomedicine combines drug delivery and diagnostic features, allowing for real-time monitoring of analgesic efficacy in vivo using molecular imaging. However, clinical translation of these nanomedicines are challenging due to complex manufacturing methodologies, lack of standardized quality control, and potentially high costs. Quality by Design (QbD) can navigate these challenges and lead to the development of an optimal pain nanomedicine. Our lab previously reported a macrophage-targeted perfluorocarbon nanoemulsion (PFC NE) that demonstrated analgesic efficacy across multiple rodent pain models in both sexes. Here, we report PFC-free, biphasic nanoemulsions formulated with a biocompatible and non-immunogenic plant-based coconut oil loaded with a COX-2 inhibitor and a clinical-grade, indocyanine green (ICG) near-infrared fluorescent (NIRF) dye for parenteral theranostic analgesic nanomedicine. Methods: Critical process parameters and material attributes were identified through the FMECA (Failure, Modes, Effects, and Criticality Analysis) method and optimized using a 3 × 2 full-factorial design of experiments. We investigated the impact of the oil-to-surfactant ratio (w/w) with three different surfactant systems on the colloidal properties of NE. Small-scale (100 mL) batches were manufactured using sonication and microfluidization, and the final formulation was scaled up to 500 mL with microfluidization. The colloidal stability of NE was assessed using dynamic light scattering (DLS) and drug quantification was conducted through reverse-phase HPLC. An in vitro drug release study was conducted using the dialysis bag method, accompanied by HPLC quantification. The formulation was further evaluated for cell viability, cellular uptake, and COX-2 inhibition in the RAW 264.7 macrophage cell line. Results: Nanoemulsion droplet size increased with a higher oil-to-surfactant ratio (w/w) but was no significant impact by the type of surfactant system used. Thermal cycling and serum stability studies confirmed NE colloidal stability upon exposure to high and low temperatures and biological fluids. We also demonstrated the necessity of a solubilizer for long-term fluorescence stability of ICG. The nanoemulsion showed no cellular toxicity and effectively inhibited PGE2 in activated macrophages. Conclusions: To our knowledge, this is the first instance of a celecoxib-loaded theranostic platform developed using a plant-derived hydrocarbon oil, applying the QbD approach that demonstrated COX-2 inhibition. Full article

This study presents a detailed experimental comparison of the rotordynamic and thermal performance of automotive turbochargers supported by two distinct hydrodynamic bearing configurations: fully floating ring bearings (FFRBs) and semi-floating ring bearings (SFRBs). While both designs are widely used in commercial turbochargers, they exhibit significantly different dynamic behaviors due to differences in ring motion and fluid film interaction. A cold air-driven test rig was employed to assess vibration and temperature characteristics across a range of controlled lubricant conditions. The test matrix included oil supply pressures from 2 bar (g) to 4 bar (g) and temperatures between 30 °C and 70 °C. Rotor speeds reached up to 200 krpm (thousands of revolutions per minute), and data were collected using a high-speed data acquisition system, triaxial accelerometers, and infrared (IR) thermal imaging. Rotor vibration was characterized through waterfall and Bode plots, while jump speeds and thermal profiles were analyzed to evaluate the onset and severity of instability. The results demonstrate that the FFRB configuration is highly sensitive to oil supply parameters, exhibiting strong subsynchronous instabilities and hysteresis during acceleration–deceleration cycles. In contrast, the SFRB configuration consistently provided superior vibrational stability and reduced sensitivity to lubricant conditions. Changes in lubricant supply conditions induced a jump speed variation in floating ring bearing (FRB) turbochargers that was approximately 3.47 times larger than that experienced by semi-floating ring bearing (SFRB) turbochargers. Furthermore, IR images and oil outlet temperature data confirm that the FFRB system experiences greater heat generation and thermal gradients, consistent with higher energy dissipation through viscous shear. This study provides a comprehensive assessment of both bearing types under realistic high-speed conditions and highlights the advantages of the SFRB configuration in improving turbocharger reliability, thermal performance, and noise suppression. The findings support the application of SFRBs in high-performance automotive systems where mechanical stability and reduced frictional losses are critical. Full article

Effective postharvest cooling of olive fruit is increasingly critical under rising harvest temperatures driven by climate change. This study models passive cooling dynamics using a trait-based, mixed-effects statistical framework. Ten olive groups—representing seven cultivars and different ripening or size stages—were subjected to controlled cooling conditions. Surface temperature was recorded using infrared thermal imaging, and morphological and compositional traits were quantified. Temperature decay was modeled using Newton’s Law of Cooling, extended with a quadratic time term to capture nonlinear trajse thectories. A linear mixed-effects model was fitted to log-transformed, normalized temperature data, incorporating trait-by-time interactions and hierarchical random effects. The results confirmed that fruit weight, specific surface area (SSA), and specific heat capacity (SHC) are key drivers of cooling rate variability, consistent with theoretical expectations, but quantified here using a trait-based statistical model applied to olive fruit. The quadratic model consistently outperformed standard exponential models, revealing dynamic effects of traits on temperature decline. Residual variation at the group level pointed to additional unmeasured structural influences. This study demonstrates that olive fruit cooling behavior can be effectively predicted using interpretable, trait-dependent models. The findings offer a quantitative basis for optimizing postharvest cooling protocols and are particularly relevant for maintaining quality under high-temperature harvest conditions. Full article

With the dynamic development of imaging technologies and increasing demands in various industrial fields, neural networks are playing a crucial role in advanced design, monitoring, and analysis techniques. This review article presents the latest research advancements in neural network-based imaging, thermal, infrared, and X-ray technologies from 2005 to 2024. It focuses on two main research categories: ‘Technology’ and ‘Application’. The ‘Technology’ category includes neural network-enhanced image sensors, thermal imaging, infrared detectors, and X-ray technologies, while the ‘Application’ category is divided into image processing, robotics and design, object recognition, medical imaging, and security systems. In image processing, significant progress has been made in classification, segmentation, digital image storage, and information classification using neural networks. Robotics and design have seen advancements in mobile robots, navigation, and machine design through neural network integration. Object recognition technologies include neural network-based object detection, face recognition, and pattern recognition. Medical imaging has benefited from innovations in diagnosis, imaging techniques, and disease detection using neural networks. Security systems have improved in terms of monitoring and efficiency through neural network applications. This review aims to provide a comprehensive understanding of the current state and future directions of neural network-based imaging, thermal, infrared, and X-ray technologies. Full article

In the context of climate change, surface urban heat islands (SUHIs) have become critical factors affecting the quality of the urban built environment. However, low-precision satellite thermal infrared remote sensing is suitable for urban scales but is insufficient to reveal the spatiotemporal distribution roles of surface heat islands at the neighborhood scale. This research takes the Sipailou Campus of Southeast University as an example and employs UAV thermal infrared imaging to acquire high-precision surface temperature data. It then systematically investigates the relationship and association mechanism between the surface urban heat island intensity (SUHII) and campus 2D/3D landscape configuration. The results indicate that the campus has a cooling effect during the daytime, with an average SUHII of −0.90 °C. It demonstrates the SUHII characteristics for campus land use types are as follows: SUHII_BD > SUHII_IS > SUHII_GS > SUHII_WB. Furthermore, the campus landscape has a significant hierarchical driving effect on SUHII, with the configuration of campus buildings and the impervious surface driving the strong heat island (SHI) and the 3D configuration and structure of greenspace dominantly strengthening the strong cool island (SCI). The overall design strategy of “two-dimensional priority, three-dimensional optimization” enables us to effectively mitigate the campus SUHII. This study reveals the spatiotemporal distribution characteristics of campus SUHII and the key influencing factors, and it also broadens the application of UAV thermal infrared imaging technology in the meso–micro-scale urban heat island assessment, providing suggestions for constructing a climate-adaptive urban landscape. Full article

The increasing demand for optical technologies with dynamic spectral control has driven interest in chromogenic materials, particularly for applications in tunable infrared metasurfaces. Phase-change materials such as vanadium dioxide and germanium–antimony–tellurium, for instance, have been widely used in the infrared regime. However, their reliance on thermal and electrical tuning introduces challenges such as high power consumption, limited emissivity tuning, and slow modulation speeds. Photochromic materials may offer an alternative approach to dynamic infrared metasurfaces, potentially overcoming these limitations through rapid, light-induced changes in their optical properties. This manuscript explores the potential of thiazolothiazole-embedded polymers, known for their reversible photochromic transitions and strong infrared absorption changes, for use in tunable infrared metasurfaces. The material exhibits low absorption and a strong photochromic contrast in the spectral range from 1500 ${\mathrm{cm}}^{-1}$ to 1700 ${\mathrm{cm}}^{-1}$, making it suitable for dynamic infrared light control. This manuscript reports on infrared imaging experiments demonstrating the photochromic contrast in thiazolothiazole-embedded polymer, and thereby provides compelling evidence for its potential applications in dynamic infrared metasurfaces. Full article

As high-specification structures, civil aircraft hangars face significant fire risks, including rapid fire propagation and challenging rescue operations. The structural integrity of these hangars is compromised under high temperatures, potentially leading to collapse and making aircraft parking and maintenance unfeasible. The severe consequences of fire in such environments make effective detection essential for mitigating risks and enhancing flight safety. However, conventional fire detectors often suffer from false alarms and missed detections, failing to meet the fire safety demands of large buildings. Additionally, many existing fire detection models are computationally intensive and large in size, posing deployment challenges in resource-limited environments. To address these issues, this paper proposes an improved YOLOv8-based lightweight model for fire detection in aircraft hangars (AH-YOLO). A custom infrared fire dataset was collected through controlled burn experiments in a real aircraft hangar, using infrared thermal imaging cameras for their long-range detection, high accuracy, and robustness to lighting conditions. First, the MobileOne module is integrated to reduce the network complexity and improve the computational efficiency. Additionally, the CBAM attention mechanism enhances fine target detection, while the improved Dynamic Head boosts the target perception. The experimental results demonstrate that AH-YOLO achieves 93.8% mAP@0.5 on this custom dataset, a 3.6% improvement over YOLOv8n while reducing parameters by 15.6% and increasing frames per second (FPS) by 19.0%. Full article

Object detection benefits greatly from multimodal image fusion, which integrates complementary data from different modalities like RGB and thermal images. However, existing methods struggle with effective inter-modal fusion, particularly in capturing spatial and contextual information across diverse regions and scales. To address these limitations, we propose the dynamic channel adjustment and multi-scale activated attention mechanism network (MNCM). Our approach incorporates dynamic channel adjustment for precise feature fusion across modalities and a multi-scale attention mechanism to capture both local and global contexts. This design improves robustness while balancing computational efficiency. The model’s scalability is enhanced through its ability to adaptively process multi-scale information without being constrained by fixed-scale designs. To validate our method, we used two multimodal datasets from traffic and industrial scenarios, which consisted of paired thermal infrared and visible light images. The results first demonstrate strong performance in multimodal fusion and then show state-of-the-art results in object detection, proving its effectiveness for real-world applications. Full article

A platform for converting near-infrared (NIR) laser power modulation into the self-mixing (SM) signal of a quantum cascade laser (QCL) operating at terahertz (THz) frequencies is introduced. This approach is based on laser feedback interferometry (LFI) with a THz QCL using a metal-coated silicon nitride trampoline membrane resonator as both the external QCL laser cavity and the mechanical coupling element of the two-laser hybrid system. We show that the membrane response can be controlled with high precision and stability both in its dynamic (i.e., piezo-electrically actuated) and static state via photothermally induced NIR laser excitation. The responsivity to nanometric external cavity variations and robustness to optical feedback of the QCL LFI apparatus allows a highly sensitive and reliable transfer of the NIR power modulation into the QCL SM voltage, with a bandwidth limited by the thermal response time of the membrane resonator. Interestingly, a dual information conversion is possible thanks to the accurate thermal tuning of the membrane resonance frequency shift and displacement. Overall, the proposed apparatus can be exploited for the precise opto-mechanical control of QCL operation with advanced applications in LFI imaging and spectroscopy and in coherent optical communication. Full article

Division-of-focal-plane (DoFP) polarization imaging systems have demonstrated considerable promise in target detection and tracking in complex backgrounds. However, existing methods face challenges, including dependence on complex image preprocessing procedures and limited real-time performance. To address these issues, this study presents a novel histogram of polarization gradient (HPG) feature descriptor that enables efficient feature representation of polarization mosaic images. First, a polarization distance calculation model based on normalized cross-correlation (NCC) and local variance is constructed, which enhances the robustness of gradient feature extraction through dynamic weight adjustment. Second, a sparse Laplacian filter is introduced to achieve refined gradient feature representation. Subsequently, adaptive polarization channel correlation weights and the second-order gradient are utilized to reconstruct the degree of linear polarization (DoLP). Finally, the gradient and DoLP sign information are ingeniously integrated to enhance the capability of directional expression, thus providing a new theoretical perspective for polarization mosaic image structure analysis. The experimental results obtained using a self-developed long-wave infrared DoFP polarization thermal imaging system demonstrate that, within the same FBACF tracking framework, the proposed HPG feature descriptor significantly outperforms traditional grayscale {8.22%, 2.93%}, histogram of oriented gradient (HOG) {5.86%, 2.41%}, and mosaic gradient histogram (MGH) {27.19%, 18.11%} feature descriptors in terms of precision and success rate. The processing speed of approximately 20 fps meets the requirements for real-time tracking applications, providing a novel technical solution for polarization imaging applications. Full article

The advancement of technology has ushered in remote sensing with the adoption of high-altitude infrared thermal object detection to leverage the distinct advantages of high-altitude platforms. These new technologies readily capture the thermal signatures of objects from an elevated point, generally unmanned aerial vehicles or drones, and thus allow for the enhancement of the detection and monitoring of extensive areas. This study explores the application of YOLOv8’s advanced architecture, as well as dynamic magnitude-based pruning techniques paired with non-maximum suppression for high-altitude infrared thermal object detection using UAVs. The current research addresses the complexities of processing high-resolution thermal imagery, where traditional methods fall short. We converted dataset annotations from the COCO and PASCAL VOC formats to YOLO’s required format, enabling efficient model training and inference. The results demonstrate the proposed architecture’s superior speed and accuracy, effectively handling thermal signatures and object detection. Precision–recall metrics indicate robust performance, though some misclassification, particularly for persons, suggests areas for further refinement. This work highlights the advanced architecture of YOLOv8’s potential in enhancing UAV-based thermal imaging applications, paving the way for more effective real-time object detection solutions. Full article

The Taylor–Quinney coefficient (TQC) is a critical parameter quantifying the thermal conversion of plastic work during deformation in metallic crystals. This review provides a comprehensive summary of recent advances in TQC research, spanning experimental, theoretical, and computational perspectives. The fundamental principles of the TQC are introduced, emphasizing its thermodynamic background and dependence on microstructural features. Experimental studies demonstrate how the strain rate, temperature, and microstructure influence the TQC, with advanced techniques such as infrared thermography and high-speed imaging enabling precise measurements under dynamic conditions. Theoretical models, including internal variable frameworks and nonequilibrium thermodynamics, offer insights into the energy distribution mechanisms and provide predictive capabilities across diverse loading scenarios. Computational simulations, using methods like finite element analysis and molecular dynamics, reveal multiscale thermal conversion mechanisms and the role of dislocation motion and localized heat accumulation in governing TQC values. Challenges and opportunities for TQC research are highlighted, including the need for multiscale modeling, the exploration of complex stress states, and applications under extreme environments. Future directions should focus on integrating advanced experimental techniques and computational models to optimize material design and performance. This review aims to deepen the understanding of the TQC and its implications for energy dissipation and material reliability in high-performance applications. Full article