Search Results (729)

The use of drones for monitoring mammal populations has increased in recent years due to their relatively low cost, accessibility, and ability to survey large areas quickly and efficiently. The type of drone sensor used during surveys can significantly influence species detection probability. For arboreal mammals, thermal infrared (TIR) sensors are commonly used because they can detect heat signatures of canopy-dwelling species. However, drones equipped with TIR cameras are more expensive and thus less accessible to conservation practitioners who often work with limited funding compared to drones equipped exclusively with standard visual spectrum cameras (Red, Green, Blue; RGB drones). Although RGB drones may represent a viable low-cost alternative for wildlife monitoring, their effectiveness for monitoring arboreal mammals remains poorly understood. Our objective was to evaluate the use of RGB drones for monitoring arboreal mammals, focusing on Geoffroy’s spider monkeys (Ateles geoffroyi) and southern muriquis (Brachyteles arachnoides). We used pre-programmed flights for spider monkeys and manual flights for muriquis, selecting the most suitable method according to the landscape characteristics of each study site; flat terrain with relatively homogeneous forest canopy height and mountainous forests with highly variable canopy height, respectively. We detected spider monkeys in only 0.4% of the 232 flights, whereas we detected muriquis in 6.2% of the 113 flights. Considering that both species are highly arboreal, use the upper canopy, and share similar locomotion patterns and group size, differences in detectability are more likely related to the type of drone flights used in each case study than to species differences. Preprogrammed flights allow for systematic and efficient area coverage but limit real-time adjustments to environmental conditions such as wind, canopy structure, and visibility. In contrast, manual flights offer greater flexibility, with pilots being able to adjust speed, height, and flight path as needed and spend more time over specific areas to conduct a more exhaustive search. This flexibility likely contributed to the higher detection rate observed in the muriqui study, but detectability was still low. The findings of the two studies suggest that RGB drones are better suited as a complementary tool rather than a primary method for monitoring arboreal mammals in dense forest habitats. Nonetheless, RGB drones offer valuable opportunities for other applications, and we highlight several examples of their potential utility in arboreal mammal research and conservation. 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

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

The present study reports the sono-chemical synthesis of novel nanosized Cr(III), Mn(II), and Pd(II) complexes incorporating the chloro-2-(quinolin-8-yliminomethyl)-phenol imine ligand. The synthesized complexes were characterized using various spectroscopic and analytical techniques, including Fourier-transform infrared (FT-IR) spectroscopy, ultraviolet–visible (UV–Vis) spectroscopy, scanning electron microscopy (SEM), and thermal gravimetric analysis (TGA). The results confirmed the successful coordination of the ligand-to-metal centers, forming stable nanosized metal complexes with distinct physicochemical properties. Biological evaluations, including antimicrobial and antioxidant assays, revealed that the synthesized complexes exhibited enhanced biological activity compared to the free ligand, demonstrating potent antibacterial and antifungal properties against various pathogenic strains. The potential of the complexes to serve as efficient free-radical inhibitors was determined by employing DPPH radical scavenging assays, which underscored their significant antioxidant properties. Furthermore, molecular docking studies were conducted to elucidate the binding interactions of the metal complexes with biological targets, providing insights into their mechanism of action. The findings suggest that the synthesized nanosized Cr(III), Mn(II), and Pd(II) complexes possess promising biological properties, making them potential candidates for pharmaceutical and biomedical applications. The study also demonstrates the effectiveness of sono-chemical synthesis in producing nanosized metal complexes with enhanced physicochemical and biological characteristics. Full article

In this study, multifunctional nanocomposite membranes were fabricated using biopolymeric polylactic acid (PLA) and cellulose acetate (CA) composites via electrospinning. The hydrophobic nanocomposite membranes were reinforced with varying concentrations of silicon dioxide (silica/SiO₂) nanoparticles. The developed PLA–CA–SiO₂ nanofibrous membranes are characterized using field emission scanning electron microscopy (FE- energy-dispersive SEM), energy-dispersive X-ray (EDX), elemental mapping, X-ray diffraction analysis (XRD), Fourier-transform infrared spectroscopy (FT–IR), thermal gravimetric analysis (TGA), and differential scanning calorimetry (DSC) techniques. Various physical and mechanical properties of the bio-nanocomposite membrane, such as tensile testing, infrared thermal imaging, ultraviolet–visible spectroscopy (UV–Vis), water contact angle, hydrostatic pressure resistance, and breathability are also investigated. The analysis revealed that a small concentration of silica nanoparticles improves the morphological, mechanical, and thermal characteristics of nanocomposite membranes. The addition of silica nanoparticles improves the UV (A & B), visible and infrared blocking efficiency while also enhancing the waterproofness of protective textiles. The PLA–CA–SiO₂ biopolymer nanocomposite membrane has a fibrous microstructure and demonstrated the tensile strength of 11.2 MPa, a Young’s modulus of 329 MPa, an elongation at break of 98.5%, a hydrostatic pressure resistance of 27 kPa, and a water contact angle of 143.7°. The developed electrospun composite membranes with improved properties provide strong potential to replace petroleum-based membranes with biopolymer-based alternatives, promising improved and wider usage for bio-related applications. 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

Lock-in thermography is a widely adopted infrared nondestructive testing technique that detects subsurface defects by applying modulated thermal waves and analyzing the resulting surface temperature variations. However, quantitatively characterizing subsurface defects at varying depths remains a significant challenge. This study explores the lateral resolution of subsurface defect detection using phase-based lock-in thermography, integrating analytical modeling, finite element simulation, and phase difference analysis. The results demonstrate that defect visibility and boundary definition are highly influenced by the excitation frequency. The thermal diffusion length, which is inversely proportional to the square root of the excitation frequency, governs both the penetration depth and the lateral spread of thermal energy. Higher frequencies enhance lateral resolution, whereas lower frequencies improve the detectability of deeper defects. Detection becomes particularly difficult for defects with small radii or low radius-to-depth ratios. A critical radius-to-depth threshold of 2 is identified as essential for reliable boundary delineation. These findings offer practical guidance for selecting excitation frequencies to achieve an optimal balance between depth sensitivity and lateral resolution in thermal-wave-based nondestructive evaluation. Full article

Background/Objectives: The rapid emergence of multidrug-resistant bacterial infections demands innovative non-antibiotic therapeutic strategies. Dual-modal photoresponse therapy integrating photodynamic (PDT) and photothermal (PTT) effects offers a promising rapid antibacterial approach, yet designing single-material systems with synergistic enhancement remains challenging. This study aims to develop uniform Cu-based metal–organic framework micrometer cubes (Cu-BN) for efficient PDT/PTT synergy. Methods: Cu-BN cubes were synthesized via a one-step hydrothermal method using Cu(NO₃)₂ and 2-amino-p-benzoic acid. The material’s dual-mode responsiveness to visible light (420 nm) and near-infrared light (808 nm) was characterized through UV–Vis spectroscopy, photothermal profiling, and reactive oxygen species (ROS) generation assays. Antibacterial efficacy against multidrug-resistant Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) was quantified via colony counting under dual-light irradiation. Results: Under synergistic 420 + 808 nm irradiation for 15 min, Cu-BN (200 μg/mL) achieved rapid eradication of multidrug-resistant E. coli (99.94%) and S. aureus (99.83%). The material reached 58.6 °C under dual-light exposure, significantly exceeding single-light performance. Photodynamic analysis confirmed a 78.7% singlet oxygen (¹O₂) conversion rate. This enhancement stems from PTT-induced membrane permeabilization accelerating ROS diffusion, while PDT-generated ROS sensitized bacteria to thermal damage. Conclusions: This integrated design enables spatiotemporal PDT/PTT synergy within a single Cu-BN system, establishing a new paradigm for rapid-acting, broad-spectrum non-antibiotic antimicrobials. The work provides critical insights for developing light-responsive biomaterials against drug-resistant infections. Full article

Aiming to address the problems of asynchronous acquisition time of multiple sensors in the crop phenotype acquisition system and high cost of the acquisition equipment, this paper developed a low-cost crop phenotype synchronous acquisition system based on the PTP synchronization protocol, realizing the synchronous acquisition of three types of crop data: visible light images, thermal infrared images, and laser point clouds. The paper innovatively proposed the Difference Structural Similarity Index Measure (DSSIM) index, combined with statistical indicators (average point number difference, average coordinate error), distribution characteristic indicators (Charm distance), and Hausdorff distance to characterize the stability of the system. After 72 consecutive hours of synchronization testing on the timing boards, it was verified that the root mean square error of the synchronization time for each timing board reached the ns level. The synchronous trigger acquisition time for crop parameters under time synchronization was controlled at the microsecond level. Using pepper as the crop sample, 133 consecutive acquisitions were conducted. The acquisition success rate for the three phenotypic data types of pepper samples was 100%, with a DSSIM of approximately 0.96. The average point number difference and average coordinate error were both about 3%, while the Charm distance and Hausdorff distance were only 1.14 mm and 5 mm. This system can provide hardware support for multi-parameter acquisition and data registration in the fast mobile crop phenotype platform, laying a reliable data foundation for crop growth monitoring, intelligent yield analysis, and prediction. Full article

Electrochromic devices (ECDs) capable of modulating both visible color and infrared (IR) emissivity are promising for applications in smart thermal camouflage and multifunctional displays. However, conventional transmissive ECDs suffer from limited IR modulation due to the low IR transmittance of transparent electrodes. Here, we report a reflection-type electrochromic zinc-ion battery (HWEC-ZIB) using a self-doped polyaniline nanorod film (SP(ANI-MA)) as the active layer. By positioning the active material at the device surface, this structure avoids interference from transparent electrodes and enables broadband and efficient IR emissivity tuning. To prevent electrolyte-induced IR absorption, a thermal lamination encapsulation method is employed. The optimized device achieves emissivity modulation ranges of 0.28 (3–5 μm) and 0.19 (8–14 μm), delivering excellent thermal camouflage performance. It also exhibits a visible color change from earthy yellow to deep green, suitable for various natural environments. In addition, the HWEC-ZIB shows a high areal capacity of 72.15 mAh cm⁻² at 0.1 mA cm⁻² and maintains 80% capacity after 5000 cycles, demonstrating outstanding electrochemical stability. This work offers a versatile device platform integrating IR stealth, visual camouflage, and energy storage, providing a promising solution for next-generation adaptive camouflage and defense-oriented electronics. Full article

This study explores the application of low-power vibrothermography (LVT) for detecting barely visible impact damage (BVID) in carbon fibre-reinforced polymer (CFRP) laminates. Composite specimens with varying impact energies (2.5–20 J) were excited using a single piezoelectric transducer with a nominal centre frequency of 28 kHz, operated at a fixed excitation frequency of 28 kHz. Thermal data were captured using an infrared camera. To enhance defect visibility and suppress background noise, the raw thermal sequences were processed using principal component analysis (PCA) and robust principal component analysis (RPCA). In LVT, RPCA and PCA provided comparable signal-to-noise ratios (SNR), with no consistent advantage for either method across all cases. In contrast, for pulsed thermography (PT) data, RPCA consistently resulted in higher SNR values, except for one sample. The LVT results were further validated by comparison with PT and phased array ultrasonic testing (PAUT) data to confirm the location and shape of detected damage. These findings demonstrate that LVT, when combined with PCA or RPCA, offers a reliable method for identifying BVID and can support safer, more efficient structural health monitoring of composite materials. Full article

Firstly, this paper reviews the fundamental theories of solid surface wettability and contact angle hysteresis. Subsequently, it further introduces four typical wettability-engineered surfaces with low hysteresis (superhydrophobic, superamphiphobic, super-slippery, and liquid-like smooth surfaces). Finally, it focuses on the latest research progress in the field of droplet manipulation on open planar surfaces with engineered wettability. To achieve droplet manipulation, the core driving forces primarily stem from natural forces guided by bioinspired gradient surfaces or the regulatory effects of external fields. In terms of bioinspired self-propelled droplet movement, this paper summarizes research inspired by natural organisms such as desert beetles, cacti, self-aligning floating seeds of emergent plants, or water-walking insects, which construct bioinspired special gradient surfaces to induce Laplace pressure differences or wettability gradients on both sides of droplets for droplet manipulation. Moreover, this paper further analyzes the mechanisms, advantages, and limitations of these self-propelled approaches, while summarizing the corresponding driving force sources and their theoretical formulas. For droplet manipulation under external fields, this paper elaborates on various external stimuli including electric fields, thermal fields, optical fields, acoustic fields, and magnetic fields. Among them, electric fields involve actuation mechanisms such as directly applied electrostatic forces and indirectly applied electrocapillary forces; thermal fields influence droplet motion through thermoresponsive wettability gradients and thermocapillary effects; optical fields cover multiple wavelengths including near-infrared, ultraviolet, and visible light; acoustic fields utilize horizontal and vertical acoustic radiation pressure or acoustic wave-induced acoustic streaming for droplet manipulation; the magnetic force acting on droplets may originate from their interior, surface, or external substrates. Based on these different transport principles, this paper comparatively analyzes the unique characteristics of droplet manipulation under the five external fields. Finally, this paper summarizes the current challenges and issues in the research of droplet manipulation on the open planar surfaces and provides an outlook on future development directions in this field. Full article

Functional flours, high in bioactive compounds, have garnered increasing attention, driven by consumer demand for alternative ingredients and the nutritional limitations of wheat flour. This study explores the thermal stability of phenolic compounds in various functional flours using visible, near and shortwave-infrared (Vis–NIR–SWIR) spectroscopy (350–2500 nm), integrated with machine learning (ML) algorithms. Random Forest models were employed to classify samples based on flour type, baking temperature, and phenolic concentration. The full spectral range yielded high classification accuracy (0.98, 0.98, and 0.99, respectively), and an explainability framework revealed the wavelengths most relevant for each class. To address concerns regarding color as a confounding factor, a targeted spectral refinement was implemented by sequentially excluding the visible region. Models trained on the 1000–2500 nm and 1400–2500 nm ranges showed minor reductions in accuracy, suggesting that classification is not solely driven by visible characteristics. Results indicated that legume and wheat flours retain higher total phenolic content (TPC) under mild thermal conditions, whereas grape seed flour (GSF) and olive stone flour (OSF) exhibited notable thermal stability of TPC even at elevated temperatures. These first findings suggest that the proposed non-destructive spectroscopic approach enables rapid classification and quality assessment of functional flours, supporting future applications in precision food formulation and quality control. Full article

Infrared thermography is a common approach used in building inspection for identifying building envelope thermal anomalies that cause energy loss and occupant thermal discomfort. Detecting these anomalies is essential to improve the thermal performance of energy-inefficient buildings through energy retrofit design and correspondingly reduce operational energy costs and environmental impacts. A thermal bridge is an unwanted conductive heat transfer. On the other hand, an infiltration/exfiltration anomaly is an uncontrollable convective heat transfer, typically happening around windows and doors, but it can also be due to a defect that comprises a building envelope’s integrity. While the existing literature underscores the significance of automatic thermal anomaly identification and offers insights into automated methodologies, there is a notable gap in addressing an automated workflow that leverages building envelope component segmentation for enhanced detection accuracy. Consequently, an automatic thermal anomaly identification workflow from visible and thermal images was developed to test it, utilizing segmented building envelope information compared to a workflow without any semantic segmentation. Therefore, building envelope images (e.g., walls and windows) were segmented based on a U-Net architecture compared to a more conventional semantic segmentation approach. The results were discussed to better understand the importance of the availability of training data and for scaling the workflow. Then, thermal anomaly thresholds for different target domains were detected using probability distributions. Finally, thermal anomaly masks of those domains were computed. This study conducted a comprehensive examination of a campus building in Syracuse, New York, utilizing a drone-based data collection approach. The case study successfully detected diverse thermal anomalies associated with various envelope components. The proposed approach offers the potential for immediate and accurate in situ thermal anomaly detection in building inspections. Full article

Infrared and visible image fusion (IVIF) endeavors to amalgamate the thermal radiation characteristics from infrared images with the fine-grained texture details from visible images, aiming to produce fused outputs that are more robust and information-rich. Among the existing methodologies, those based on generative adversarial networks (GANs) have demonstrated considerable promise. However, such approaches are frequently constrained by their reliance on homogeneous discriminators possessing identical architectures, a limitation that can precipitate the emergence of undesirable artifacts in the resultant fused images. To surmount this challenge, this paper introduces HCSPNet, a novel GAN-based framework. HCSPNet distinctively incorporates heterogeneous dual discriminators, meticulously engineered for the fusion of disparate source images inherent in the IVIF task. This architectural design ensures the steadfast preservation of critical information from the source inputs, even when faced with scenarios of image degradation. Specifically, the two structurally distinct discriminators within HCSPNet are augmented with adaptive salient information distillation (ASID) modules, each uniquely structured to align with the intrinsic properties of infrared and visible images. This mechanism impels the discriminators to concentrate on pivotal components during their assessment of whether the fused image has proficiently inherited significant information from the source modalities—namely, the salient thermal signatures from infrared imagery and the detailed textural content from visible imagery—thereby markedly diminishing the occurrence of unwanted artifacts. Comprehensive experimentation conducted across multiple publicly available datasets substantiates the preeminence and generalization capabilities of HCSPNet, underscoring its significant potential for practical deployment. Additionally, we also prove that our proposed heterogeneous dual discriminators can serve as a plug-and-play structure to improve the performance of existing GAN-based methods. Full article