Search Results (908)

Unoccupied Aerial Systems (UASs) offer great potential for monitoring breeding colonial seabirds. However, survey flights need to be planned carefully to maximize detection of birds, allow for reliable counts, and minimize disturbance. In this study, we evaluated UAS-based monitoring for the most numerous seabird species in the Faroe Islands, the northern fulmar (Fulmarus glacialis), assessing both disturbance and optimal viewing angles. We found that behavioral disturbance could be minimized by adhering to a set of strict operating protocols, including strategic and flexible flight paths that ensured UAS distances remained above vigilance thresholds, allowing for initial habituation and limiting responses to the presence of the UAS. During surveys, quantifiable behavioral alterations (vigilance) were observed at distances ≤57.5 m in mixed areas containing both incubating and resting individuals, and ≤32.9 m in areas with only incubating individuals. At greater distances, only light responses (head turning) occurred. To optimize monitoring efficiency, we found that a slight downward camera tilt of −13.8° consistently provided the highest bird visibility, detecting 93% of individuals. Complete visibility was achieved by covering a range from −30° to −1.3°, depending on terrain type and bird age group, highlighting the observation angle as a critical factor for reliable surveys in the investigated complex topography. Overall, these results will provide a strong foundation for further research into tailored flight and survey protocols for cliff-nesting seabirds utilizing UAS technology. Full article

Maritime search and rescue is an important component of emergency response frameworks and primarily relies on Unmanned Aerial Vehicles (UAVs) for maritime object detection. However, maritime accidents frequently occur in low-visibility environments, such as foggy or low-light conditions, which lead to low contrast, blurred object boundaries, and degraded texture representations. Most existing maritime object detection algorithms are developed for natural light scenes, and their performance deteriorates markedly when deployed directly in low-visibility environments, primarily due to reduced image quality that hinders feature extraction and semantic information aggregation. Although several studies incorporate image enhancement techniques prior to detection to improve image quality, these approaches often introduce significant additional computational overhead, limiting their practical deployment on UAV platforms. To tackle these challenges, this paper proposes a lightweight model built upon a recent YOLO framework, termed Multi-Scale Adaptive YOLO (MSA-YOLO), for maritime detection using UAVs in low-visibility environments. The proposed model systematically optimizes the backbone, neck, and detection head networks. Specifically, an improved StarNet backbone is designed by integrating Efficient Channel Attention (ECA) mechanisms and multi-scale convolutional kernels, which strengthen feature extraction capability while maintaining low computational overhead. In the neck network, a high-frequency enhanced residual block branch is inserted into the C3k2 module to capture richer detailed information, while depthwise separable convolution is utilized to further reduce computational cost. Moreover, a non-parametric attention module is incorporated into the detection head to adaptively optimize features in the classification and regression branches. Finally, a joint loss function that combines bounding box regression, classification, and distribution focal losses is utilized to improve detection accuracy and training stability. Experimental results on the constructed AFO, Zhoushan Island, and Shandong Province datasets demonstrate that, relative to YOLOv11-s, MSA-YOLO reduces model parameters and FLOPs by 52.07% and 41.36%, respectively, while achieving improvements of 1.11% and 1.33% in mAP@0.5:0.95 and mAP@0.5. These results indicate that the proposed method effectively balances computational efficiency and detection accuracy, rendering it suitable for practical maritime search and rescue applications in low-visibility environments. Full article

Cloud detection is a fundamental task in atmospheric science and satellite remote sensing. While numerous algorithms utilizing multiple visible and infrared channels have been developed, the absence of visible light at night forces most current methods to rely on multi-channel thermal infrared (TIR) observations. Consequently, detection accuracy is significantly reduced due to the minimal thermal contrast between low clouds and the ground. Furthermore, distinguishing clouds under strictly moonless conditions remains a critical challenge. Leveraging the low-light observation capability of the Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS/DNB), this study proposes a single-channel cloud detection algorithm. Based on the physical scattering of ground-based artificial lights by clouds, the algorithm integrates a feature-engineering layer with a Random Forest machine learning model. This moonlight-independent approach can rapidly determine cloudy conditions, offering a novel method for high-precision nighttime cloud detection. Validation experiments using a single fixed radar site in Longmen, China, with 97 rigorously synchronized satellite-radar sample pairs, demonstrate that the proposed algorithm achieves an overall accuracy of 86.6% (95% CI: 78.4–92.0%) against millimeter-wave cloud radar observations. While strictly reliant on stable artificial ground lights—making it primarily applicable to urban and artificially lit regions—this method provides a valuable supplementary tool for nighttime monitoring. Full article

Indoor visible light communication (VLC) has attracted increasing attention as a promising wireless access technology because of its large unlicensed bandwidth and dual functionality of illumination and data transmission. However, practical VLC systems are vulnerable to line-of-sight (LoS) blockage caused by user mobility, human shadowing, and indoor obstacles, which may degrade link reliability and service continuity. Although hybrid VLC/RF networks can improve robustness by using RF transmission as a backup link, excessive RF fallback under severe optical blockage may overload the bandwidth-limited RF interface and reduce the service quality of RF-associated users. To address this issue, this paper investigates a risk-aware illumination-constrained resource allocation scheme for hybrid VLC/RF indoor networks under random optical blockage. A unified system model is developed by considering Lambertian optical propagation, random optical blockage, RF backup transmission, and working-plane illumination constraints. Based on this model, a joint user association and power allocation problem is formulated under QoS, transmit-power, and illumination requirements. The proposed scheme evaluates VLC service utility under blockage uncertainty, controls RF fallback to avoid excessive backup-link loading, allocates VLC/RF transmission power, and performs illumination feasibility adjustment to preserve the required lighting level. Simulation results show that, under severe blockage conditions, the proposed scheme reduces the outage probability to approximately 0.26, compared with 0.68 for VLC-only transmission and 0.47 for threshold-based VLC/RF switching. For a 20-user network, the proposed scheme achieves an average sum rate of approximately 277 Mbps, maintains a 100% illumination compliance ratio, and achieves higher energy efficiency than the benchmark schemes. Further RF backup analysis shows that the proposed scheme can maintain the service quality of RF-associated users by avoiding excessive RF fallback. These results demonstrate the effectiveness of the proposed framework for reliable and illumination-feasible hybrid VLC/RF indoor communication. Full article

Semiconductor photocatalysis technology is a simple, efficient, and low-cost method for environmental pollution remediation. As a promising photocatalyst for oxidative degradation, titanium dioxide (TiO₂) demonstrates the capability to address energy shortages and environmental pollution issues. In this study, orange peel was used as the raw material to synthesize a (TiO₂-CdS-C₃N₄-CDs) TCCC composite photocatalyst containing natural green carbon dots via a one-pot hydrothermal method for the first time. This catalyst was applied to the catalytic degradation of multiple dye molecules (Rhodamine B, Methylene Green, Reactive Brilliant Blue KN-R) and quinolone antibiotic (Ciprofloxacin, CIP) as well as tetracycline antibiotic (Tetracycline, THC). Meanwhile, it provides more adsorption sites for target pollutants and loads electron reservoirs (CDs) on the TCC surface, promoting the separation of photogenerated carriers in pure TiO₂, thereby enhancing the visible light utilization and photocatalytic activity of the material. This work expands the application scope of semiconductor photocatalysis technology and TiO₂-based photocatalytic active substrates. Full article

Accurate meteorological visibility estimation is vital for transportation safety and environmental monitoring. However, modeling the inherent nonlinear spatial and spectral degradations in hazy environments remains challenging. While recent Large Vision-Language Models (LVLMs) offer strong scene understanding, they lack the regression precision required for visibility estimation. In this paper, we propose the Visibility-Aware Refined CNN (VISR-CNN), a dual-stream architecture that synthesizes local spatial cues with global frequency-domain signatures. The model integrates a Multi-Scale Transmission Attention (MSTA) module, which uses parallel dilated convolutions to estimate atmospheric transmission, and a Global Frequency Branch that utilizes 2D Real Fast Fourier Transforms (RFFT) with Spectral Gating to quantify visibility-dependent blurring. A progressive training strategy is introduced to decouple spectral and spatial optimization, and a physics-informed loss function is designed to supervise numerical regression while enforcing a monotonic ranking constraint consistent with physical light-attenuation laws. Results on the HKCHC-VD dataset show that VISR-CNN achieves state-of-the-art performance (MAE: 1.54 km; RMSE: 2.31 km), representing a 13.0% improvement over VisNet. Further evaluations on the CP1 and SWH datasets confirm robust generalization, reducing overall MAE by 21% and 20%, respectively, compared with the hybrid ResNeXt-50 + ViT model. Notably, in safety-critical range (0–10 km), VISR-CNN reduces RMSE for the HKCHC-VD, CP1, and SWH datasets by approximately 55%, 64%, and 71%, respectively, when compared with VisNet. These findings demonstrate the superiority of specialized, physics-grounded architectures over general-purpose LVLMs for high-precision meteorological regression. Full article

The identification of the chemical species responsible for the anomalous near-ultraviolet (UV) opacity in the Venusian cloud for “unknown absorber” remains a paramount challenge in planetary science. This study presents a comprehensive quantum chemical investigation into a broad suite of candidate molecules, including isomers of thiosulfeno (S₂O₂), the hydroxysulfonyl radical (HSO₃), disulfur monoxide (S₂O), disulfur dichloride (S₂Cl₂), iron(III) chloride (FeCl₃), phosphine (PH₃), and structural isomers of polysulfur oxides (S₃O). Utilizing Time-Dependent Density Functional Theory (TD-DFT) at the CAM-B3LYP/def2-TZVPP level of theory, we systematically mapped electronic transitions across three distinct environmental phases: gas-phase (without solvent), supercritical CO₂, and concentrated H₂SO₄ aerosols. To establish confidence in the predicted results, our TD-DFT approach was rigorously benchmarked against high-level theoretical methods (CCSD(T), EOM-CCSD, and MRCI+Q) from recent literature. All these electronic transitions were modeled via the Solvation Model based on Density (SMD). Our results demonstrate a profound topological and environmental dependence on spectral signatures. Among the candidates, trans-OSSO (t-OSSO) emerged as the most viable near-UV absorber candidate, exhibiting a highly allowed π → π* transition at 379.37 nm (f = 0.1140) in H₂SO₄, providing a near-perfect alignment with the observed 365 nm planetary albedo drop. Conversely, the polysulfur oxide cis-S₃O was acknowledged as a primary visible-light chromophore, with an intense absorption at 436.31 nm (f = 0.1280) responsible for the characteristic yellow tint of the planet. Additionally, the photochemically maintained SSCl₂ isomer was identified as a critical broadband near-UV absorber. Species such as S₂O and planar S₃O were found to function as critical mid-UV shields (270–300 nm). This work establishes a multi-chromophore model of the Venusian atmosphere, where a chemically stratified network of sulfur-oxygen chains and chlorine-sulfur reservoirs, tuned by the acidic aerosol matrix, collectively governs radiative balance and atmospheric super-rotation of the planet. Furthermore, to account for massive continuum tailing into the visible region (>400 nm), we employed a semi-classical Reflection Principle approach to model 1D vibronic broadening. This analysis revealed that while standard solvent effects induce minor solvatochromic shifts, ground-state structural fluxionality in the OSSO isomers drives intense, symmetry-allowed transitions deep into the visible spectrum, an effect absent in structurally constrained or rigid control species. Full article

GaN-on-Si light-emitting devices have been widely studied in the field of opto-electronics, while their optical performance and characterization accessibility are severely limited by the strong visible light absorption of the native silicon substrate. Conventional substrate transfer technologies often suffer from inherent thermal, optical, or mechanical bottlenecks. In this study, we developed a robust wafer-level substrate transfer strategy for 8-inch green GaN-on-Si light-emitting device wafers, utilizing a hybrid planarization process combined with SiO₂–SiO₂ direct bonding. The hybrid planarization precisely eliminated the 900 nm macroscopic steps, achieving sub-nanometer surface roughness for high-yield wafer bonding. We systematically investigated the physical evolution during substrate removal. Results indicate that the removal of the thick native silicon and high-stress buffer layers effectively released the additional in-plane biaxial compressive stress within the multiple quantum wells (MQWs), thereby mitigating the quantum-confined Stark effect (QCSE). Benefiting from the elimination of the light-absorbing silicon substrate and the incorporation of a built-in back-surface reflector (BSR), the transferred devices achieved a remarkable 1.9-fold enhancement in relative optical performance, albeit with an inherent trade-off of increased reverse leakage current while preserving basic diode functionality. Furthermore, optothermal dynamic analysis at high injection levels suggests a potential localized thermal bottleneck at the thick SiO₂ bonding interface, where a hypothesized heat-induced spectral red shift may counteract the carrier-screening blue shift. This work provides a feasible wafer-level substrate transfer process for GaN-on-Si devices and offers systematic experimental insights into stress relaxation and optothermal behaviors during the substrate transfer process. Full article

This work presents a machine-vision–based measurement and control framework for laser wire directed energy deposition (LWDED) processes. A visible-light camera system is used to capture meltpool images, from which a novel vision algorithm extracts the wire–meltpool interface location. By utilizing a camera that is rigidly mounted to the deposition head, the vision algorithm provides a relative measurement of the distance between the nozzle tip and the workpiece, also referred to as wire stickout. A proportional-derivative (PD) control strategy is implemented using the measured stickout as feedback to adjust deposition feedrate. Results show that the control system successfully compensates for improper layer height increments, enabling thin-wall builds to consistently reach target geometry. Full article

Photocatalytic oxygen reduction to hydrogen peroxide (H₂O₂) offers a promising route for sustainable chemical synthesis, yet the efficiency of carbon nitride-based photocatalysts is often limited by narrow light absorption and rapid charge recombination. Low-molecular-weight carbon nitride exhibits a favorable reduction potential but suffers from poor visible-light utilization, while π-conjugation extension and heteroatom doping are effective yet rarely combined within a single oligomeric framework. In this work, we report a low-temperature (400 °C) one-step copolymerization approach employing urea and terephthalonitrile to construct an oxygen-doped, benzene-bridged carbon nitride oligomer (O-B-CNO). Comprehensive characterization confirms the successful integration of both benzene rings and oxygen dopants into the oligomer backbone, with the former enhancing structural stability and the latter introducing active sites. The extended conjugation and oxygen incorporation synergistically modulate the electronic structure, leading to a narrowed bandgap, improved visible-light harvesting, and suppressed charge recombination. As a result, O-B-CNO delivers a photocatalytic H₂O₂ yield of approximately 3000 μM under visible-light irradiation, a 10-fold enhancement over the pristine oligomer, with optimal activity at neutral pH via the two-electron oxygen reduction pathway. The enhanced performance stems from the complementary functions of the two modifications: benzene rings promote electron delocalization and charge transport, while oxygen dopants serve as selective active centers for oxygen reduction. This work demonstrates a viable molecular engineering strategy for developing efficient carbon nitride photocatalysts for H₂O₂ production. Full article

The visible light and infrared thermal multimodal images in autonomous driving provide a wealth of information for pedestrian detection, and its challenge lies in utilizing the complementary information across modalities to obtain an optimal joint representation. This study proposes a light-aware modality balancing network (LMB-Net) for pedestrian detection by fusing visible light and infrared thermal images. We designed an alignment complementary fusion module across modalities to exchange target information. Deformable convolutions are employed to automatically perform spatial deformation on features, thereby eliminating perception biases caused by misalignment. Furthermore, as the contribution of different modalities to pedestrian detection varies under different lighting conditions, we designed a light-aware module to utilize the distinct advantages of visible light and infrared thermal images. Extensive experiments on the KAIST and LLVIP datasets demonstrate that our method achieves the best detection performance compared to some other methods. Full article

The SnO₂-TiO₂ binary nanocomposites’ metal oxide was synthesized by a co-precipitation method and potentially utilized for wastewater treatment applications. The average crystallite size, dislocation density, and micro strain of the synthesized nanocomposites were calculated by the Debye–Scherrer, modified Debye–Scherrer, and W–H methods. The nanocomposites exhibit a tetragonal crystal structure with 62% crystallinity. The presence of Ti–O–Ti and Sn–O–Sn bonds was identified using the FTIR technique. The surface morphology was examined during SEM and EDAX analyses. The optical properties were interpreted with the help of UV–Vis and PL spectroscopy, and the bandgap energy was ascertained. From the CV and EIS studies, the behavior of the diffusive and capacitive natures was determined. Photocatalytic studies were carried out under sunlight and UV light by degrading (cationic) malachite dye at concentrations of 10, 20, and 40 mg/L. When analyzed with seven kinetic models, it was inferred that a pseudo-second and first-order were followed under visible and UV light. The maximum degradation efficiency of 94% was achieved for the 20 mg/L dye concentration within 50 min under UV and 150 min under solar irradiation. Complete decolorization was observed for both 10 mg/L and 20 mg/L dye concentrations under both irradiations. Full article

Titanium-based perovskites have garnered significant attention for photocatalytic applications, particularly in the field of environmental remediation through the degradation of synthetic dyes and pharmaceuticals in aqueous solutions. This review paper aims to explore the synthesis methods, crystal structures, photoactivity, and photocatalytic performance of titanium-based perovskites in degrading synthetic dye and pharmaceutical effluents in water. The unique advantages of titanium-based perovskites as photocatalysts, associated with their high redox potentials and excellent optical and electrical properties, are highlighted. Their limitations in visible light absorption and photocatalytic efficiency due to rapid charge carrier recombination are also discussed. Several strategies to overcome these limitations, such as surface modifications of the photocatalysts, metal and non-metal doping, the introduction of structure defects, the formation of heterojunctions with electron-accepting materials, and the deposition of plasmonic metal nanoparticles are systematically examined. This review also provides an overview of the photocatalytic degradation of dyes and pharmaceuticals as emerging contaminants, utilizing titanium-based perovskites as photocatalysts, to highlight their efficiency and potential for real-word applications. By covering research findings, current knowledge, and future perspectives, this review aims to stimulate advancements in the design and application of titanium-based perovskite photocatalysts. Full article

Effective monitoring of surface damage in mining areas is vital for ecological restoration. Unmanned aerial vehicles (UAVs) have been widely used to obtain ground subsidence data owing to their low cost and ease of operation. The images captured by UAVs can generate dense image matching (DIM) point clouds, which, after screening, can be used to create a digital elevation model (DEM) required for deformation analysis. Existing filtering algorithms mainly rely on the spatial geometric features of point clouds and rarely utilize color information, which limits their accuracy in areas with vegetation coverage. To address this issue, this study proposes a H-PTD method that combines visible light with progressive triangulated irregular network densification (PTD). First, initial ground seeds are selected based on the H value in the HSV space. Subsequently, a triangulated irregular network (TIN) is constructed, and iterative densification is performed by evaluating the relationship between the target point and adjacent triangular faces, thereby achieving an accurate distinction between ground and non-ground. Evaluated on three terrain datasets and against five classical methods, the results indicate that the Total error in the H-PTD cross-matrix is controlled between 2.9% and 7.8%, and remains below 8% overall. The standard deviation of the DEM difference is around 0.02 m. Compared to other methods, H-PTD shows higher filtering accuracy and better terrain adaptability, making it more promising for monitoring mining areas and providing a more reliable tool for subsidence detection based on UAVs. Full article

Airfield lighting control (ALC) is critical for ensuring safe, efficient, and compliant airport operations, especially under low-visibility conditions. However, current centralized control architectures cannot adequately meet the real-time responsiveness, scalability, and reliability requirements of Advanced Surface Movement Guidance and Control Systems (A-SMGCS) Level IV. To overcome these limitations, this paper proposes a novel cloud–edge–end collaborative architecture for a mobile ALC scenario, in which we formulate a joint task computing and energy consumption optimization problem to maximize long-term system utility under latency, computation, and communication constraints. In this way, the mobile airfield lighting (MAL) system can also quickly adapt its optimal formation pattern based on the airport environment, lighting conditions, and the type of aircraft taking off or landing via efficient computation, thereby achieving the best navigational assistance effect. For solving such an optimization problem, a framework that combines K-medoids with the Improved Twin Delayed Deep Deterministic Policy Gradient (ITD3) is proposed to integrate the efficiency of clustering for rough allocation and the high-precision dynamic optimization capability of the improved TD3. The training depends on edge nodes and the cloud to achieve online performance. Finally, the extensive simulation proved that our novel algorithm is efficient. Full article