Search Results (8,434)

Detecting small moving objects under challenging lighting conditions, such as overexposure and underexposure, remains a critical challenge in computer vision applications including surveillance, autonomous driving, and anti-UAV systems. Traditional RGB-based detectors often suffer from degraded object visibility and highly dynamic illumination, leading to suboptimal performance. To address these limitations, we propose a novel RGB-Event fusion framework that leverages the complementary strengths of RGB and event modalities for enhanced small object detection. Specifically, we introduce a Temporal Multi-Scale Attention Fusion (TMAF) module to encode motion cues from event streams at multiple temporal scales, thereby enhancing the saliency of small object features. Furthermore, we design a Sparse Noisy Gated Attention Fusion (SNGAF) module, inspired by the mixture-of-experts paradigm, which employs a sparse gating mechanism to adaptively combine multiple fusion experts based on input characteristics, enabling flexible and robust RGB-Event feature integration. Additionally, we present RGBE-UAV, which is a new RGB-Event dataset tailored for small moving object detection under diverse exposure conditions. Extensive experiments on our RGBE-UAV and public DSEC-MOD datasets demonstrate that our method outperforms existing state-of-the-art RGB-Event fusion approaches, validating its effectiveness and generalization under complex lighting conditions. Full article

We fabricated dye-sensitized solar cells (DSSCs) co-sensitized with the organic dye YD2 and a spiropyran derivative (SPNO₂), a photochromic molecule capable of reversible isomerization under light irradiation. Upon UV exposure, SPNO₂ converts from its closed spiropyran (SP) form to the open photomerocyanine (PMC) form, which absorbs visible light and changes the optical properties of the photoelectrode. Spectroscopic analysis showed an 18% decrease in transmittance at 540 nm after UV irradiation and a 10% increase following visible light exposure. These changes were accompanied by a 0.5% increase in power conversion efficiency (η) after 5 min of UV irradiation, and a 0.83% decrease after 10 min of visible light. Although direct electron injection from PMC into TiO₂ appears inefficient, the enhanced performance is attributed to Förster resonance energy transfer (FRET) from PMC to YD2. This photoresponsive behavior highlights a co-sensitization strategy that combines dynamic optical control and efficient energy transfer. Our findings demonstrate a promising approach to designing smart DSSCs with externally tunable photovoltaic properties using photochromic sensitizers. Full article

This study leverages multi-source remote sensing data—Nighttime Light (NTL) imagery and POI (Point of Interest) datasets—to quantify the spatiotemporal interaction between urban spatial restructuring and logistics industry evolution in Zhengzhou, China. Using calibrated NPP/VIIRS NTL data (2012–2022) and fine-grained POI data, we (1) identified urban functional spaces through kernel density-based spatial grids weighted by public awareness parameters; (2) extracted built-up areas via the dynamic adaptive threshold segmentation of NTL gradients; (3) analyzed logistics agglomeration dynamics using emerging spatiotemporal hotspot analysis (ESTH) and space–time cube models. The results show that Zhengzhou’s urban form transitioned from a monocentric to a polycentric structure, with NTL trajectories revealing logistics hotspots expanding along air–rail multimodal corridors. POI-derived functional spaces shifted from single-dominant to composite patterns, while ESTH detected policy-driven clusters in Airport Economic Zones and market-driven suburban cold chain hubs. Bivariate LISA confirmed the spatial synergy between logistics growth and urban expansion, validating the “policy–space–industry” interaction framework. This research demonstrates how integrated NTL-POI remote sensing techniques can monitor policy impacts on urban systems, providing a replicable methodology for sustainable logistics planning. Full article

Background/Objectives: Increasing the bioavailability of poorly absorbed drugs is a continuous challenge in modern pharmaceutical technology. This is due to the problematic nature of BCS class IV active pharmaceutical ingredients: these drugs possess poor solubility and membrane permeability. Moreover, many undergo immediate efflux and/or rapid systemic metabolism after absorption. This project aimed to improve the bioavailability of BCS class IV drugs by formulating gastroretentive self-emulsifying systems using curcumin as a model drug. Methods: The base of the systems was created by melting emulsifying agents, dissolution retardants, and PEGs together. Curcumin was added after the mixture was cooled slightly. Aqueous dispersions of several compositions were characterized by dynamic light scattering. After screening these results, the viscosities of the selected formulations were evaluated. Dissolution retardants were selected and added to the most superior samples, and their dissolution profiles were compared. Gastroretention of the final formulation was achieved by dispersing air in the molten system through melt foaming; internal structure was assessed by microCT, and physicochemical properties by PXRD and DSC. Cytotoxicity was measured in Caco-2 cells using MTT and Neutral Red assays, and transcellular transport was also studied. Results: Based on these results, a homogeneous gastric floating system was developed. We observed an advantageous cytotoxic profile and increased bioavailability. Conclusions: Overall, we were able to create a self-emulsifying gastroretentive formulation displaying extended release and gastric retention with a low amount of cost-efficient excipients. Full article

Polymer-modified bitumen is difficult to produce and often separates during storage and transport. In contrast, granular bitumen modifiers offer wide applicability, construction flexibility, and ease of transport and storage. This study involved preparing a multipolymer granulated bitumen modifier with a styrene–butadiene–styrene block copolymer, polyethylene, and aromatic oil. To elucidate the modification mechanism of a multipolymer granulated bitumen modifier on bitumen, the elemental composition of bitumen A and B, the micro-morphology of the modifiers, the changes in functional groups, and the distribution state of the polymers in the bitumen were investigated using an elemental analyzer, a scanning electron microscope, Fourier-transform infrared spectroscopy, and fluorescence microscopy. The effects of the multipolymer granulated bitumen modifier on the physical, rheological, and viscoelastic properties of two types of base bituminous binders were investigated at various dosages. The test results show that the Z_H/C ratio of base bitumen A is smaller than that of base bitumen B and that the cross-linking effect with the polymer is optimal. Therefore, the direct-feed modified asphalt of A performs better than the direct-feed modified asphalt of B under the same multipolymer granulated bitumen modifier content. The loose, porous surface structure of styrene–butadiene–styrene block copolymer promotes the adsorption of light components in bitumen, and the microstructure of the multipolymer granulated bitumen modifier is highly coherent. When the multipolymer granulated bitumen modifier content is 20%, the physical, rheological, and viscoelastic properties of the direct-feed modified asphalt of A/direct-feed modified asphalt of B and the commodity styrene–butadiene–styrene block copolymer are essentially identical. While the multipolymer granulated bitumen modifier did not significantly improve the performance of bitumen A/B at contents greater than 20%, the mass loss rate of the direct-feed modified asphalt of A to aggregate stabilized, and the adhesion effect reached stability. Image processing determined the optimum mixing temperature and time for multipolymer granulated bitumen modifier and aggregate to be 185–195 °C and 80–100 s, respectively, at which point the dispersion homogeneity of the multipolymer granulated bitumen modifier in the mixture was at its best. The dynamic stability, fracture energy, freeze–thaw splitting strength ratio, and immersion residual stability of bitumen mixtures were similar to those of commodity styrene–butadiene–styrene block copolymers with a 20% multipolymer granulated bitumen modifier mixing amount, which was equivalent to the wet method. The styrene–butadiene–styrene block copolymer bitumen mixture reached the same technical level. Full article

To meet the vibration isolation requirements of engines under diverse operating conditions, this paper proposes a novel magnetorheological fluid-elastomer isolator with high load and tunable parameters. The mechanical and magnetic circuit structures of the isolator were designed and optimized through theoretical calculations and finite element simulations, achieving effective vibration isolation within confined spaces. The dynamic performance of the isolator was experimentally evaluated using a hydraulic testing system under varying excitation amplitudes, frequencies, initial positions, and magnetic fields. Experimental results indicate that the isolator achieves a static stiffness of 3 × 10⁶ N/m and a maximum adjustable compression load range of 105.4%. In light of the asymmetric nonlinear dynamic behavior of the isolator, an improved nine-parameter Bouc–Wen model is proposed. Parameter identification performed via a genetic algorithm demonstrates a model accuracy of 95.0%, with a minimum error reduction of 28.8% compared to the conventional Bouc–Wen model. Full article

Effective control of calcium carbonate (CaCO₃) scale formation is crucial to improve the performance and economic efficiency of water systems. This study investigates the impact of various scale inhibitors on the nucleation and crystallization processes of CaCO₃. Calcium carbonate particles were synthesized by mixing CaCl₂·2H₂O and NaHCO₃ solutions, in the presence of various scale inhibitors that had not previously been investigated using the experimental techniques employed in this study. Particle size distribution and zeta potential were analyzed using dynamic light scattering (DLS), while Ca⁺² consumption and pH changes were monitored with ion-selective electrodes. Crystal morphology was evaluated using scanning electron microscopy (SEM) and cryo-transmission electron microscopy (cryo-TEM). We demonstrated that, in all samples, approximately 98% of the CaCO₃ particles (sized between 400 and 840 nm) are formed within the first 30 min of synthesis, and these particles then aggregate to form larger particles (840–1100 nm in size). Due to the solution’s high supersaturation, the inhibitors influence calcium consumption only after 5 min of synthesis. All inhibitors, especially DTPMP, decrease calcium consumption and particle size during synthesis. The zeta potential and morphology of the particles in the samples containing inhibitors differed from those in the control group. Cryo-TEM observations revealed distinct nanometric precursor phases in the calcite crystallization process without inhibitors and different nanostructures when scale inhibitors were used. Moreover, conchoidal fractures were observed in the nanoparticles formed in the presence of DTPMP. This study demonstrates the effectiveness of various inhibitors in reducing calcium consumption in solution and altering the morphology of CaCO₃ crystals, thereby preventing calcium carbonate (CaCO₃) scale formation. Full article

Autonomous docking is a critical capability for enabling fully automated operations in industrial and logistics environments using Autonomous Mobile Robots (AMRs). Traditional rule-based docking approaches often struggle with generalization and robustness in complex, dynamic scenarios. This paper presents a deep reinforcement learning-based autonomous docking framework that integrates Proximal Policy Optimization (PPO) with multi-sensor fusion. It includes YOLO-based vision detection, depth estimation, and LiDAR-based orientation correction. A concise 4D state vector, comprising relative position and angle indicators, is used to guide a continuous control policy. The outputs are linear and angular velocity commands for smooth and accurate docking. The training is conducted in a Gym-compatible Gazebo simulation, acting as a digital twin of the real-world system, and incorporates realistic variations in lighting, obstacle placement, and marker visibility. A designed reward function encourages alignment accuracy, progress, and safety. The final policy is deployed on a real robot via a sim-to-real transfer pipeline, supported by a ROS-based transfer node. Experimental results demonstrate that the proposed method achieves robust and precise docking behavior under diverse real-world conditions, validating the effectiveness of PPO-based learning and sensor fusion for practical autonomous docking applications. Full article

The nitrogen-vacancy (NV) center magnetic sensor, leveraging nitrogen-vacancy quantum effects, enables high-sensitivity magnetic field detection via optically detected magnetic resonance (ODMR). However, conventional single-point integrated devices suffer from limitations such as inefficient regional magnetic field detection and challenges in discerning the directional variations of dynamic magnetic fields. To address these issues, this study proposes an array- based architecture that innovatively substitutes the conventional 532 nm laser with quantum-dot light-emitting diodes (QLEDs). Capitalizing on the advantages of QLEDs—including compatibility with micro/nano-fabrication processes, wavelength tunability, and high luminance—a 2 × 2 monolithically integrated magnetometer array was developed. Each sensor unit achieves a magnetic sensitivity of below 26 nT·Hz^−1/2 and a measurable range of ±120 μT within the 1–10 Hz effective bandwidth. Experimental validation confirms the array’s ability to simultaneously resolve multi-regional magnetic fields and track dynamic field orientations while maintaining exceptional device uniformity. This advancement establishes a scalable framework for the design of large-scale magnetic sensing arrays, demonstrating significant potential for applications requiring spatially resolved and directionally sensitive magnetometry. Full article

Background/Objectives: Fenbendazole is a potential cancer treatment and a proven antiparasitic in veterinary applications. However, its poor water solubility limits its application. In this study, potential fenbendazole solubility enhancement was investigated through size reduction methods. The effect of the presence of Soluplus^® on solubility was investigated as well. Methods: Solubility enhancement was explored using microfluidization and ultrasonication techniques. These techniques were applied to fenbendazole alone and in combination with Soluplus^®. UV–Vis spectroscopy was used to determine solubility. Possible chemical reactions were checked using Fourier transform infrared spectroscopy (FT-IR). Differential scanning calorimetry (DSC) was conducted to analyze the physical structure and crystallinity of the samples. Scanning electron microscopy (SEM) was also utilized for characterization of the effect of the treated formulations and the size reduction method on morphology. The elements present in samples were identified with energy-dispersive X-ray spectroscopy (EDX) combined with SEM. A comparison of crystalline structure between the products was performed via X-ray powder diffraction (XRPD). Dynamic light scattering (DLS) was also used to measure the samples’ average particle size at different stages. Results: Both ultrasonication and microfluidization led to marginal increases in the solubility of neat fenbendazole. In contrast, formulations processed in the presence of Soluplus^® demonstrated a greater enhancement in solubility. However, solubility improvement was not retained in the dried samples. The post-drying samples, irrespective of the presence of Soluplus^®, showed nearly the same solubility as neat fenbendazole. Conclusions: Size-reduction methods, particularly when combined with Soluplus^®, improved the solubility of fenbendazole. However, drying appeared to reverse these gains, regardless of the method used. Full article

Structure and nonlinear spectra of the basal surface of ice Ih were investigated by molecular dynamics simulations. At a temperature significantly lower than the melting temperature $T_m$, the ice structure at the interface is only weakly perturbed by the presence of surface. The computed nonlinear spectrum of the interface well agrees with the experimental data and the results of the calculations provide the molecular-level interpretation of spectral features. In particular, the ice surface specific positive peaks in the Im[${\chi }^{\left(2\right)}$] spectrum at ∼3180 cm⁻¹ and at ∼3420 cm⁻¹ were found to result from the low- and high-frequency vibrational modes of quadruply H-bonded surface molecules, respectively. The spectrum of the crystalline ice interface is significantly affected by intermolecular interactions. Upon increasing the temperature, the structural disorder extends to the second water bilayer. The thickness of the premelted water layer of 6–8 Å can be estimated at the temperature by ca. 5 K below $T_m$. The increase in the temperature results in a change in the intensity and shape of the nonlinear spectrum of the ice Ih interface. The changes can be explained by the interconversion between different H-bonded surface species and by an increase in disordering of water molecules that reduces strength of intermolecular interactions. Results of the present work contribute to our understanding of the structure–spectrum relationship of the ice/air interface, and shed light on the origins of features in the nonlinear spectra of the system. Full article

Nitrosamines, including N-nitroso diethylamine (NDEA) have emerged as pharmaceutical impurities and carcinogenic environmental contaminants of grave public health safety concerns. This study reports on the preparation and first use of cysteine–gold nanoparticles (CysAuNPs) for colorimetric detection of NDEA in human serum albumin (HSA) under physiological conditions. Molecular docking (MD) and molecular dynamic simulation (MDS) were performed to probe the interaction between NDEA and serum albumin. UV–visible absorption and fluorescence spectroscopy, dynamic light scattering (DLS), and transmission electron microscopy (TEM) imaging were used to characterize the synthesized CysAuNPs. These CysAuNPs show a UV–visible absorbance wavelength maxima (λ_max) at 377 nm and emission λ_max at 623 nm. Results from DLS measurement revealed the CysAuNPs’ uniform size distribution and high polydispersity index of 0.8. Microscopic imaging using TEM showed that CysAuNPs have spherical to nanoplate-like morphology. The addition of NDEA to HSA in the presence of CysAuNPs resulted in a remarkable increase in the absorbance of human serum albumin. The interaction of NDEA–CysAuNPs–HSA is plausibly facilitated by hydrogen bonding, sulfur linkages, or by Cys–NDEA-induced electrostatic and van der Waal interactions. These are due to the disruption of the disulfide bond linkage in Cys–Cys upon the addition of NDEA, causing the unfolding of the serum albumin and the dispersion of CysAuNPs. The combined use of molecular dynamic simulation and colorimetric experiment provided complementary data that allows robust analysis of NDEA in serum samples. In addition, the low cost of the UV–visible spectrophotometer and the easy preparation and optical sensitivity of CysAuNPs sensors are desirable, allowing the low detection limit of the CysAuNPs sensors, which are capable of detecting as little as 0.35 µM NDEA in serum albumin samples, making the protocol an attractive sensor for rapid detection of nitrosamines in biological samples. Full article

Fine-grained plant image classification (FPIC) aims to distinguish plant species with subtle visual differences, but existing datasets often suffer from limited category diversity, homogeneous backgrounds, and insufficient environmental variation, limiting their effectiveness in complex real-world scenarios. To address these challenges, a novel dataset, FewMedical-XJAU, is presented, focusing on rare medicinal plants native to Xinjiang, China. This dataset offers higher intra-class variability, more complex and diverse natural backgrounds, varied shooting angles and lighting conditions, and more rigorous expert annotations, providing a realistic testbed for FPIC tasks. Building on this, an improved method called BDCC (Bilinear Deep Cross-modal Composition) is proposed, which incorporates textual priors into a deep metric learning framework to enhance semantic discrimination. A Class-Aware Structured Text Prompt Construction strategy is introduced to improve the model’s semantic understanding, along with a dynamic fusion mechanism to address high inter-class similarity and intra-class variability. In few-shot classification experiments, the method demonstrates superior accuracy and robustness under complex environmental conditions, offering strong support for practical applications of fine-grained classification. Full article

Efficient navigation of Automated Guided Vehicles (AGVs) in dynamic warehouse environments requires real-time and accurate path segmentation algorithms. However, traditional semantic segmentation models suffer from excessive parameters and high computational costs, limiting their deployment on resource-constrained embedded platforms. A lightweight image segmentation algorithm is proposed, built on an improved ESPNet-C architecture, combining Spatial Group-wise Enhance (SGE) and Efficient Channel Attention (ECA) with a dual-branch upsampling decoder. On our custom warehouse dataset, the model attains 90.5% Miou with 0.425 M parameters and runs at ~160 FPS, reducing parameters by ×116–×136 and computational costs by 70–92% in comparison with DeepLabV3+. The proposed model improves boundary coherence by 22% under uneven lighting and achieves 90.2% Miou on the public BDD100K benchmark, demonstrating strong generalization beyond warehouse data. These results highlight its suitability as a real-time visual perception module for AGV navigation in resource-constrained environments and offer practical guidance for designing lightweight semantic segmentation models for embedded applications. Full article

AI-integrated robotics in Industry 5.0 demands advanced manufacturing systems capable of autonomously interpreting complex geometries and dynamically adjusting machining strategies in real time—particularly when dealing with aerospace components featuring large-curvature surfaces. Large-curvature aerospace components present significant challenges for precision drilling due to surface-normal deviations caused by curvature, roughness, and thin-wall deformation. This study presents a robotic drilling system that integrates adaptive PCA-based surface normal estimation with in-process pre-drilling correction and post-drilling verification. This system integrates a 660 nm wavelength linear laser projector and a 1.3-megapixel industrial camera arranged at a fixed 30° angle, which project and capture structured-light fringes. Based on triangulation, high-resolution point clouds are reconstructed for precise surface analysis. By adaptively selecting localized point-cloud regions during machining, the proposed algorithm converts raw measurements into precise normal vectors, thereby achieving an accurate solution of the normal direction of the surface of large curvature parts. Experimental validation on a 400 mm-diameter cylinder shows that using point clouds within a 100 mm radius yields deviations within an acceptable range of theoretical normals, demonstrating both high precision and reliability. Moreover, experiments on cylindrical aerospace-grade specimens demonstrate normal direction accuracy ≤ 0.2° and hole position error ≤ 0.25 mm, maintained across varying curvature radii and roughness levels. The research will make up for the shortcomings of existing manual drilling methods, improve the accuracy of hole-making positions, and meet the high fatigue service needs of aerospace and other industries. This system is significant in promoting the development of industrial automation and improving the productivity of enterprises by improving drilling precision and repeatability, enabling reliable assembly of high-curvature aerospace structures within stringent tolerance requirements. Full article