Search Results (455)

This study employs the improved modified extended tanh method (IMETM) to derive exact analytical solutions of a higher-order nonlinear Schrödinger (HNLS) model, incorporating $\beta$-fractional derivatives in both time and space. Unlike classical methods such as the inverse scattering transform or Hirota’s bilinear technique, which are typically limited to integrable systems and integer-order operators, the IMETM offers enhanced flexibility for handling fractional models and higher-order nonlinearities. It enables the systematic construction of diverse solution types—including Weierstrass elliptic, exponential, Jacobi elliptic, and bright solitons—within a unified algebraic framework. The inclusion of fractional derivatives introduces richer dynamical behavior, capturing nonlocal dispersion and temporal memory effects. Visual simulations illustrate how fractional parameters $\alpha$ (space) and $\beta$ (time) affect wave structures, revealing their impact on solution shape and stability. The proposed framework provides new insights into fractional NLS dynamics with potential applications in optical fiber communications, nonlinear optics, and related physical systems. Full article

Carboxymethyl cellulose (CMC) films, derived from sugarcane bagasse agricultural waste (SCB) incorporated with Betalains-nitrogen-doped carbon dots (Betalains-N–CQDs), derived from beet root waste (BR), offer a sustainable, smart and naked-eye sensor for strawberry packaging due to their excellent fluorescent and shape memory properties. These CMC-Betalains-N–CQDs aim to enhance strawberry preservation and safety by enabling visual detection of common food contaminants such as bacteria, fungi and Pb(II). Crucially, the CMC-Betalains-N–CQD film also exhibits excellent shape memory properties, capable of fixing various shapes under alkaline conditions and recovering its original form in acidic environments, thereby offering enhanced physical protection for delicate produce like strawberries. Optical studies reveal the Betalains-N–CQDs’ pH-responsive fluorescence, with distinct emission patterns observed across various pH levels, highlighting their potential for sensing applications. Scanning Electron Microscopy (SEM) confirms the successful incorporation of Betalains-N–CQDs into the CMC matrix, revealing larger pores in the composite film that facilitate better interaction with analytes such as bacteria. Crucially, the CMC-Betalains-N–CQD film demonstrates significant antibacterial activity against common foodborne pathogens like Escherichia coli, Staphylococcus aureus, and Candida albicans, as evidenced by inhibition zones and supported by molecular docking simulations showing strong binding interactions with bacterial proteins. Furthermore, the film functions as a fluorescent sensor, exhibiting distinct color changes upon contact with different microorganisms and Pb(II) heavy metals, enabling rapid, naked-eye detection. The film also acts as a pH sensor, displaying color shifts (brown in alkaline, yellow in acidic) due to the betalains, useful for monitoring food spoilage. This research presents a promising, sustainable, and multifunctional intelligent packaging solution for enhanced food safety and extended shelf life. Full article

Synthetic Aperture Radar (SAR) images and optical imagery have high potential for extracting digital elevation models (DEMs). The two main approaches for deriving elevation models from SAR data are interferometry (InSAR) and radargrammetry. Adapted from photogrammetric principles, radargrammetry relies on disparity model estimation as its core component. Matching strategies in radargrammetry typically follow local, global, or semi-global methodologies. Local methods, while having higher accuracy, especially in low-texture SAR images, require larger kernel sizes, leading to quadratic computational complexity. Conversely, global and semi-global models produce more consistent and higher-quality disparity maps but are computationally more intensive than local methods with small kernels and require more memory (RAM). In this study, inspired by the advantages of local matching algorithms, a computationally efficient and novel model is proposed for extracting corresponding pixels in SAR-intensity stereo images. To enhance accuracy, the proposed two-stage algorithm operates without an image pyramid structure. Notably, unlike traditional local and global models, the computational complexity of the proposed approach remains stable as the input size or kernel dimensions increase while memory consumption stays low. Compared to a pyramid-based local normalized cross-correlation (NCC) algorithm and adaptive semi-global matching (SGM) models, the proposed method maintains good accuracy comparable to adaptive SGM while reducing processing time by up to 50% relative to pyramid SGM and achieving a 35-fold speedup over the local NCC algorithm with an optimal kernel size. Validated on a Sentinel-1 stereo pair with a 10 m ground-pixel size, the proposed algorithm yields a DEM with an average accuracy of 34.1 m. Full article

Accurate estimation of upper-limb joint angles is essential in biomechanics, rehabilitation, and wearable robotics. While inertial measurement units (IMUs) offer portability and flexibility, systems requiring multiple inertial sensors can be intrusive and complex to deploy. In contrast, optical motion capture (MoCap) systems provide precise tracking but are constrained to controlled laboratory environments. This study presents a deep learning-based approach for estimating shoulder and elbow joint angles using only three IMU sensors positioned on the chest and both wrists, validated against reference angles obtained from a MoCap system. The input data includes Euler angles, accelerometer, and gyroscope data, synchronized and segmented into sliding windows. Two recurrent neural network architectures, Convolutional Neural Network with Long-short Term Memory (CNN-LSTM) and Bidirectional LSTM (BLSTM), were trained and evaluated using identical conditions. The CNN component enabled the LSTM to extract spatial features that enhance sequential pattern learning, improving angle reconstruction. Both models achieved accurate estimation performance: CNN-LSTM yielded lower Mean Absolute Error (MAE) in smooth trajectories, while BLSTM provided smoother predictions but underestimated some peak movements, especially in the primary axes of rotation. These findings support the development of scalable, deep learning-based wearable systems and contribute to future applications in clinical assessment, sports performance analysis, and human motion research. Full article

This paper presents the flow field reconstruction and prediction of powder fuel transport systems based on representative feature extraction from scattering images using deep learning techniques. A laboratory-built powder fuel supply system was used to conduct scattering spectroscopy experiments on boron-based fuel under various flow rate conditions. Based on the acquired scattering images, a prediction and reconstruction method was developed using a deep network framework composed of a Stacked Autoencoder (SAE), a Backpropagation Neural Network (BP), and a Long Short-Term Memory (LSTM) model. The proposed framework enables accurate classification and prediction of the dynamic evolution of flow structures based on learned representations from scattering images. Experimental results show that the feature vectors extracted by the SAE form clearly separable clusters in the latent space, leading to high classification accuracy under varying flow conditions. In the prediction task, the feature vectors predicted by the LSTM exhibit strong agreement with ground truth, with average mean square error, mean absolute error, and r-square values of 0.0027, 0.0398, and 0.9897, respectively. Furthermore, the reconstructed images offer a visual representation of the changing flow field, validating the model’s effectiveness in structure-level recovery. These results suggest that the proposed method provides reliable support for future real-time prediction of powder fuel mass flow rates based on optical sensing and imaging techniques. Full article

This study presents a theoretical model for the thermoelastic response in transmission-mode photoacoustic systems that feature a two-layer structure. The model incorporates volumetric optical absorption in both layers and is based on classical heat conduction theory, hyperbolic generalized heat conduction theory, and fractional heat conduction models including inertial memory in Generalizations of the Cattaneo Equation (GCEI, GCEII, and GCEIII). To validate the model, comparisons were made with the existing literature models. Using the proposed model, the thermoelastic photoacoustic response of a two-layer system composed of a 3D-printed porous polyamide (PA12) substrate coated with a thin, highly absorptive protective dye layer is analyzed. We obtain that the thickness and thermal conduction in properties of the coating are very important in influencing the thermoelastic component and should not be overlooked. Furthermore, the thermoelastic component is affected by the selected fractional model—whether it is subdiffusion or superdiffusion—along with the value of the order of the fractional derivative, as well as the optical absorption coefficient of the layer being investigated. Additionally, it is concluded that the phase has a greater impact than the amplitude when selecting the appropriate theoretical heat conduction model. Full article

Adaptive digital back-propagation (A-DBP) is a promising candidate for mitigating Kerr nonlinearity due to its ability to estimate the optimal nonlinear scaling factor adaptively. However, the adaptive process relying on the gradient-dependent algorithm is prone to fluctuation, leading to extra iterations or even divergence and resulting in huge computational efforts in A-DBP. In this paper, an improved A-DBP algorithm with long-term memory (LTM) is proposed, employing root mean square propagation (RMSProp) to achieve low-complexity and high-robustness compensation performances. The A-DBP-LTM algorithm based on RMSProp was numerically validated through the simulated transmission of 69 Gbaud DP-16QAM over 2000 km and further verified through an experiment involving 26-λ 63 Gbaud DP-16QAM transmission over 1200 km. Compared with conventional digital back-propagation and A-DBP based on a gradient-descent algorithm, our proposed method allows substantial complexity reductions of 31.35% and 58.47%, respectively. Furthermore, high robustness in only a few iterations and a 0.33 dB improvement in the optical signal–noise ratio penalty were also experimentally demonstrated. Full article

This study addresses the challenges of redundant crop identification features and low computational efficiency in complex agricultural environments, particularly in arid regions. Focusing on the Hexi region of Gansu Province, we utilized the Google Earth Engine (GEE) to integrate Sentinel-2 optical imagery (10 bands) and Sentinel-1 radar data (VV/VH polarization), constructing a 96-feature set that comprises spectral, vegetation index, red-edge, and texture variables. The recursive feature elimination random forest (RF-RFE) algorithm was employed for feature selection and model optimization. Key findings include: (1) Variables driven by spatiotemporal differentiation were effectively selected, with red-edge bands (B5–B7) during the grain-filling stage in August accounting for 56.7% of the top 30 features, which were closely correlated with canopy chlorophyll content (p < 0.01). (2) A breakthrough in lightweight modeling was achieved, reducing the number of features by 69%, enhancing computational efficiency by 62.5% (from 8 h to 3 h), and decreasing memory usage by 66.7% (from 12 GB to 4 GB), while maintaining classification accuracy (PA: 97.69%, UA: 97.20%, Kappa: 0.89). (3) Multi-source data fusion improved accuracy by 11.54% compared to optical-only schemes, demonstrating the compensatory role of radar in arid, cloudy regions. This study offers an interpretable and transferable lightweight framework for precision crop monitoring in arid zones. Full article

This paper presents FWStab, a specialized video stabilization dataset tailored for flapping-wing platforms. The dataset encompasses five typical flight scenarios, featuring 48 video clips with intense dynamic jitter. The corresponding Inertial Measurement Unit (IMU) sensor data are synchronously collected, which jointly provide reliable support for multimodal modeling. Based on this, to address the issue of poor image acquisition quality due to severe vibrations in aerial vehicles, this paper proposes a multi-modal signal fusion video stabilization framework. This framework effectively integrates image features and inertial sensor features to predict smooth and stable camera poses. During the video stabilization process, the true camera motion originally estimated based on sensors is warped to the smooth trajectory predicted by the network, thereby optimizing the inter-frame stability. This approach maintains the global rigidity of scene motion, avoids visual artifacts caused by traditional dense optical flow-based spatiotemporal warping, and rectifies rolling shutter-induced distortions. Furthermore, the network is trained in an unsupervised manner by leveraging a joint loss function that integrates camera pose smoothness and optical flow residuals. When coupled with a multi-stage training strategy, this framework demonstrates remarkable stabilization adaptability across a wide range of scenarios. The entire framework employs Long Short-Term Memory (LSTM) to model the temporal characteristics of camera trajectories, enabling high-precision prediction of smooth trajectories. Full article

Titanium dioxide (TiO₂) is a wide-bandgap semiconductor material with broad application potential, known for its excellent photocatalytic performance, high chemical stability, low cost, and non-toxicity. These properties make it highly attractive for applications in photovoltaic energy, environmental remediation, and optoelectronic devices. For instance, TiO₂ is widely used as a photocatalyst for hydrogen production via water splitting and for degrading organic pollutants, thanks to its efficient photo-generated electron–hole separation. Additionally, TiO₂ exhibits remarkable performance in dye-sensitized solar cells and photodetectors, providing critical support for advancements in green energy and photoelectric conversion technologies. Boron-doped diamond (BDD) is renowned for its exceptional electrical conductivity, high hardness, wide electrochemical window, and outstanding chemical inertness. These unique characteristics enable its extensive use in fields such as electrochemical analysis, electrocatalysis, sensors, and biomedicine. For example, BDD electrodes exhibit high sensitivity and stability in detecting trace chemicals and pollutants, while also demonstrating excellent performance in electrocatalytic water splitting and industrial wastewater treatment. Its chemical stability and biocompatibility make it an ideal material for biosensors and implantable devices. Research indicates that the combination of TiO₂ nanostructures and BDD into heterostructures can exhibit unexpected optical and electrical performance and transport behavior, opening up new possibilities for photoluminescence and rectifier diode devices. However, applications based on this heterostructure still face challenges, particularly in terms of photodetector, photoelectric emitter, optical modulator, and optical fiber devices under high-temperature conditions. This article explores the potential and prospects of their combined heterostructures in the field of optoelectronic devices such as photodetector, light emitting diode (LED), memory, field effect transistor (FET) and sensing. TiO₂/BDD heterojunction can enhance photoresponsivity and extend the spectral detection range which enables stability in high-temperature and harsh environments due to BDD’s thermal conductivity. This article proposes future research directions and prospects to facilitate the development of TiO₂ nanostructured materials and BDD-based heterostructures, providing a foundation for enhancing photoresponsivity and extending the spectral detection range enables stability in high-temperature and high-frequency optoelectronic devices field. Further research and exploration of optoelectronic devices based on TiO₂-BDD heterostructures hold significant importance, offering new breakthroughs and innovations for the future development of optoelectronic technology. Full article

We report the fabrication and characterization of Al-doped ZnO (AZO) optoelectronic synaptic devices based on sol–gel-derived thin films with varying Al concentrations (0~4.0 wt%). Structural and optical analyses reveal that moderate Al doping modulates the crystal orientation, optical bandgap, and defect levels of ZnO films. Notably, 2.0 wt% Al doping yields the widest bandgap (3.31 eV), stable PL emission, and uniform deep-level absorption without inducing significant lattice disorder. Synaptic performance, including learning–forgetting dynamics and persistent photoconductivity (PPC), is strongly dependent on Al concentration. The 2.0 wt% AZO device exhibits the lowest forgetting rate and longest memory retention due to optimized trap formation, particularly Al–oxygen vacancy complexes that enhance carrier lifetime. Visual memory simulations using a 3 × 3 pixel array under patterned UV illumination further confirm superior long-term memory (LTM) behavior at 2.0 wt%, with stronger excitatory postsynaptic current (EPSC) retention during repeated stimulation. These results demonstrate that precise doping control via the sol–gel method enables defect engineering in oxide-based neuromorphic devices. Our findings provide an effective strategy for designing low-cost, scalable optoelectronic synapses with tunable memory characteristics suitable for future in-sensor computing and neuromorphic vision systems. Full article

The development of artificial intelligence has resulted in significant challenges to conventional von Neumann architectures, including the separation of storage and computation, and power consumption bottlenecks. The new generation of brain-like devices is accelerating its evolution in the direction of high-density integration and integrated sensing, storage, and computing. The structural and information transmission similarity between memristors and biological synapses signifies their unique potential in sensing and memory. Therefore, memristors have become potential candidates for neural devices. In this paper, we have designed an optoelectronic memristor based on a ZnO/Cu₂O structure to achieve synaptic behavior through the modulation of electrical signals, demonstrating the recognition of a dataset by a neural network. Furthermore, the optical synaptic functions, such as short-term/long-term potentiation and learn-forget-relearn behavior, and advanced synaptic behavior of optoelectronic modulation, are successfully simulated. The mechanism of light-induced conductance enhancement is explained by the barrier change at the interface. This work explores a new pathway for constructing next-generation optoelectronic synaptic devices, which lays the foundation for future brain-like visual chips and intelligent perceptual devices. Full article

This study addresses the challenges of characterizing aerosol optical depth (AOD) dynamics from satellite observations, which are often hindered by data gaps and variability. A long short-term memory (LSTM) network was trained on an extended AOD dataset from Sicily to capture temporal patterns. The trained model was then applied to AOD data from distinct geographical regions: Cluj-Napoca and the central Mediterranean Sea. While the LSTM effectively captured general seasonal trends, it tended to smooth extreme AOD events. To mitigate this, a post-processing algorithm was developed to enhance the representation of AOD peaks and valleys. This enhancement method refines the characterization of historical AOD, providing a more accurate representation of observed atmospheric variability, particularly in capturing high and low AOD episodes. The results demonstrate the efficacy of the hybrid approach in improving the characterization of AOD dynamics across different regions. Full article

With the worldwide spread of clear aligner treatment (CAT), a plethora of new thermoplastics is currently commercially available on the market, claiming to have superior properties and greater comfort. This review aims to summarise the properties of the materials and their effects on treatment effectiveness and comfort to ease material selection and also incorporate new emerging trends such as shape memory polymers (SMPs) and direct 3D printer aligners. First, a concise historical overview of orthodontics will be presented, along with the basic properties of thermoplastics and their importance in treatment. Following the individual properties, we present an analysis of optical, biocompatibility, and toxicity aspects, passing through others such as thermal, mechanical, and special methods to simulate in vivo measurements. We end with the impact of this technique on the environment and the advances and perspectives of CAT. Full article

Prominent target detection in optical remote sensing images (RSI-SOD) focuses on segmenting key targets that capture human attention. However, most SOD methods prioritize detection accuracy at the cost of memory. Complex backgrounds, occlusions, and noise distort segmented target boundaries, while large memory demands increase computational cost, and reduced memory impairs segmentation accuracy. To address these challenges, we integrate edge enhancement and attention mechanisms with multi-path complementary features for salient object detection in remote sensing images (IEAM), aiming to improve salient target accuracy, boundary detection, and memory efficiency. The architecture utilizes a structured feature fusion strategy, combining spatial channel attention mechanisms with adaptive merging to enhance multi-scale feature representation and suppress background noise. The Spatially Adaptive Edge Embedded Module (SAEM) refines object boundary perception, the SCAAP module dynamically selects relevant spatial and channel features while balancing adaptive and maximal pooling, and the Spatial Adaptive Guidance (SAG) module enhances feature localization in cluttered environments to mitigate semantic dilution in U-shaped networks. Extensive experiments on the EORSSD and ORSSD benchmark datasets demonstrate that IEAM outperforms 21 state-of-the-art methods, achieving an inference speed of 48 FPS at 103.2 G FLOP, making it suitable for real-time applications. The proposed model is robust and excels in multiple aspects. Full article