Search Results (4,924)

Under alkaline conditions, most commonly used preservatives exhibit limited efficacy and fail to meet the preservation requirements of coated tofu. This study aims to investigate the effects of metal-phenolic networks (MPNs) on quality deterioration, protein oxidation, conformation, and gel microstructure of coated tofu during cold storage (4 °C and 10 °C). The results showed that, compared with the untreated control group, MPNs treatment effectively inhibited protein oxidation, alleviated quality deterioration, delayed the degradation of color and texture, and reduced protein degradation, as evidenced by soluble protein contents that were 63.55% (4 °C) and 66.65% (10 °C) lower than those of the control group after 20 days of storage. MPNs treatment also improved the orderliness and stability of the protein secondary structure. In addition, electrophoretic analysis showed that MPNs markedly retarded the decline in band optical density of the 11S protein A subunit by 96.19% and 97.28% at 4 °C and 10 °C, respectively, and suppressed the increase in the B subunit by 13.28% and 73.20%, respectively. Moreover, MPNs treatment helped maintain a more compact gel network. Based on physicochemical characterization and Pearson correlation analysis, the preservative effect of MPNs on coated tofu under alkaline conditions was elucidated, revealing the internal correlation between the inhibition of quality deterioration and the regulation of protein oxidation. Specifically, MPNs mitigate protein disulfide bond loss, increase the β-sheet content, preserve the natural protein conformation and the relative proportion of 11S subunits, stabilize the gel microstructure, and thereby achieve quality preservation. These findings provide theoretical support and strategic reference for the development of preservation technologies for alkaline high-protein gel foods. Full article

Freezing rain is a high-impact winter weather phenomenon that acts as a major disturbance agent in forest ecosystems, causing canopy damage, stem breakage, tree mortality, and long-term changes in forest structure and functioning. Although ice storms have been studied for decades, research on freezing rain impacts on forests remains fragmented across multiple disciplines, and few studies have attempted an integrated synthesis that simultaneously combines climatological, ecological, and methodological perspectives. In this study, we present a systematic and integrative review of the scientific literature on freezing rain and forests, combining a large-scale bibliometric analysis with an in-depth qualitative synthesis. A total of 241 publications retrieved from the Scopus and Web of Science databases were analyzed following PRISMA guidelines. The bibliometric assessment examined publication trends, geographic distribution, institutional contributions, research domains, and keyword networks. The qualitative review synthesized current knowledge on freezing rain climatology, forest damage mechanisms, species-specific vulnerability, major ice storm events, detection and modeling approaches, and ecological consequences. Results reveal a strong increase in scientific output over the last two decades, dominated by research from North America and northern Europe. Ice accretion intensity emerges as the primary driver of forest damage, while species traits, crown architecture, tree size, stand structure, topography, and exposure strongly modulate damage severity. Freezing rain affects a wide range of forest types worldwide and triggers both immediate structural damage and long-term ecological effects, including altered successional dynamics and reduced forest productivity. Recent methodological advances—including passive remote sensing (e.g., optical satellite data), active remote sensing (e.g., LiDAR), experimental ice storm simulations, reanalysis datasets, and machine learning approaches—have significantly improved detection, monitoring, and forecasting capabilities. Despite these advances, major research gaps remain, particularly regarding long-term ecosystem recovery, trait-based vulnerability, socio-economic impacts, and future freezing rain regimes under climate change. This review highlights freezing rain as an increasingly important but underappreciated forest disturbance and underscores the need for interdisciplinary research and adaptive management strategies in ice-prone regions. Full article

Landslides are widespread geohazards in mountainous regions and pose serious threats to human safety, infrastructure, and ecosystems. Accurate detection from high-resolution optical remote sensing imagery remains challenging because landslide targets often exhibit irregular morphology, large scale variation, weak boundaries, and strong background interference. To address these issues, this study proposes L-SAINet, a shape-adaptive and inner-scale interaction network for landslide detection in complex remote sensing scenarios. Built on a lightweight one-stage detection framework, the proposed method introduces an L-SAI module that integrates adaptive deformable convolution, channel–spatial attention, and inner-scale feature interaction. The shape-adaptive branch improves geometric alignment for irregular and elongated landslide bodies, while the attention branch enhances semantic discrimination under heterogeneous background conditions. The two branches are further fused at the same feature scale to construct a more unified landslide representation. Experiments on the Bijie Landslide Remote Sensing Dataset show that L-SAINet consistently outperforms the baseline detector and single-branch variants in Precision, Recall, mAP@0.5, and mAP@0.5:0.95. Additional analyses based on precision–recall curves, confusion matrices, convergence behavior, model complexity, and representative complex-scene examples further confirm its effectiveness and robustness. The results demonstrate that jointly modeling geometric adaptability and semantic refinement is an effective strategy for landslide detection in complex mountain environments. Full article

Wormlike micelles (WLMs) of surfactants with rheological properties highly responsive to pH are of growing interest for various applications. The present paper proposes an approach to enhance their rheological properties and make the pH-response more pronounced. It consists of the incorporation of a percolated network of cellulose nanocrystals (CNCs) into the solution of entangled WLMs. To provide pH-responsiveness, potassium oleate was used as a surfactant. Rheological studies demonstrated that CNCs increase the viscosity and storage modulus by one order of magnitude. This effect was attributed to the interaction of WLMs with nanocrystals and the formation of entanglements of WLMs with percolated CNCs. Moreover, added CNCs make the pH-response stronger. The lowering of pH from 10.1 to 9.7 leads to a sharp drop in viscosity by ca. 2000 Pa·s, which is much higher than the decrease in viscosity of the WLM solution without CNCs. According to SANS data, the drop in viscosity is due to the transformation of WLMs into vesicles. It occurs as a result of the protonation of surfactant carboxylic groups decreasing surface charge on the micelles. In the presence of CNCs, the transition pH shifts to an alkaline medium, indicating that CNCs promote vesicle formation. Also, CNCs cause some of the vesicles to aggregate with each other, as follows from dynamic light scattering and optical microscopy data. Both observations suggest an interaction between CNCs and vesicles, which is supported by ITC data. These findings are valuable for the research and development of high-performing surfactant-based products. Full article

Forest, shrubland, and grassland exhibit highly overlapping characteristics, and single-modal remote sensing data cannot simultaneously capture both spectral and structural information. Moreover, multimodal fusion learning of optical and SAR data faces challenges such as the lack of high-quality samples and difficulties in effective cross-modal feature fusion. Therefore, a high-resolution multimodal remote sensing feature dataset (GF23FSG) is constructed for the fine classification of forest, shrubland, and grassland, and a Cross-modal Adaptive Structure Fusion Network (CASFNet) is proposed. In response to the feature heterogeneity of optical and SAR, a cross-modal adaptive fusion module based on spatial alignment and a dynamic weight allocation strategy is proposed, which effectively enhances the learning of spectral–spectrum heterogeneous features. In addition, a multi-level auxiliary supervision mechanism is introduced to strengthen feature representation learning. Gradient constraints are further imposed on deep-level features to improve the model’s ability to capture and learn deep cross-modal representations, thereby effectively mitigating representation degradation during the feature fusion process. Experiments on the self-constructed GF23FSG dataset and the publicly available SEN12MS dataset achieve OA of 77.38% and 71.84%, respectively, demonstrating superior classification performance compared with SOTA methods. In addition, comparative analysis with public land cover products and field samples further confirm the reliability and generalization performance of the proposed dataset and model for the fine classification of forest, shrubland, and grassland. This study provides a new solution for the fine classification of forest, shrubland, and grassland from multimodal remote sensing images from the perspectives of dataset construction and methodological design. Full article

Accurate cloud and cloud-shadow segmentation is a crucial step in optical remote sensing image preprocessing, playing a significant role in subsequent applications such as land-cover classification and change detection. However, the complexity of cloud/shadow shapes and noise interference (e.g., snow and ice, buildings, complex backgrounds, and atmospheric optics) make this task challenging. Although existing deep learning methods have achieved remarkable results in cloud segmentation tasks, a better balance between computational efficiency and segmentation accuracy is still needed. Traditional deep learning models have good detail and generalization capabilities due to their local feature extraction ability and spatial invariance, but they are relatively weak in processing global context information, leading to false positives and false negatives in complex scenarios. Encoders based on state space models (such as VMamba) can effectively capture global context through long-range dependency modeling, but there is still room for optimization in computational efficiency. Additionally, complex attention mechanisms (such as CBAM) can improve feature representation capability, but the large number of parameters limits the deployment efficiency of models. This paper conducts a systematic architectural exploration of the MCloudX cloud segmentation network, seeking a balance between efficiency and accuracy from three directions: backbone network modernization, encoder efficiency optimization, and attention mechanism lightweighting. Through comprehensive ablation experiments on SPARCS and L8-Biome datasets, we systematically evaluate the independent and synergistic effects of each component and validate them on Biome_3 and SPARCS datasets. Experimental results show that the proposed optimization configuration (ResNet50+LocalMamba+ECA-Net) significantly improves computational efficiency while maintaining comparable accuracy to the baseline. We name this optimization configuration LECloud, providing valuable empirical references for future research on efficient remote sensing segmentation architectures. Full article

Accurate target perception in unstructured outdoor environments remains a fundamental challenge in computational imaging and machine vision, primarily due to severe optical degradation caused by variable illumination, specular highlights, and dense foliage occlusion. Existing optical sensing systems often struggle to maintain robustness under these physical constraints, especially when deployed on edge devices with strict computational limits. To address these challenges, this paper proposes Orchard-YOLO, a lightweight, computationally efficient object detection network designed to maintain robustness against environmental and optical noise in complex orchard environments. Unlike generic architectures, Orchard-YOLO introduces three architectural enhancements for robust detection: (1) a High-Resolution P2 Detection Head to preserve high-frequency optical details and fine-grained texture cues often lost during digital downsampling; (2) Coordinate Attention (CA) mechanisms integrated into the feature fusion pathway to filter out background optical interference and enhance spatial discrimination for heavily occluded targets; and (3) a Ghost-convolution-based backbone to optimize the inference pipeline for real-time edge processing. Evaluated on a comprehensive multi-fruit dataset under simulated optical stress (including ±50% illumination variation and up to 70% occlusion), Orchard-YOLO achieves 94.8% mAP@0.5. It shows improved robustness under illumination variation and occlusion compared to baseline models, while achieving up to 25 FPS on an NVIDIA Jetson Nano edge device. These results suggest that Orchard-YOLO offers a detection framework suitable for resource-constrained orchard perception. Full article

Ground Control Points (GCPs) are essential for accurate georeferencing of optical imagery; however, their selection is often heuristic and affected by temporal changes in image geometry. This challenge is particularly acute for Google Earth imagery, where acquisition conditions and mosaicking processes vary over time. This paper presents a multi-epoch robust framework for the automatic design of GCP networks to precisely georeference multi-temporal Google Earth images. GCP selection is formulated within an affine optimal experimental design setting, in which candidate configurations are evaluated against the most challenging acquisition epoch to promote consistency over time. A hybrid DI-optimality criterion balances transformation stability and interior prediction accuracy without requiring interior control points. The framework also includes an automated method for determining the optimal number of GCPs using marginal-gain stopping and cost-regularized μ-sweep analysis. Experiments on two urban case studies show that compact, well-conditioned GCP networks can match the accuracy of larger heuristic networks and achieve top 10% root-mean-square error (RMSE) performance on a random feasible subset benchmark. Results demonstrate that a carefully designed GCP network can greatly reduce the number of control points needed while maintaining stable geometric performance across acquisition sessions. Full article

In micro-expression detection, head movements may seriously interfere with subtle and transient micro-expression signals, significantly limiting detection performance. Therefore, we propose a robust detection framework that integrates 3D motion correction and a Transformer network. This framework adopts a two-stage design. In the first stage, a depth-weighted optical flow method is proposed to decompose and suppress head motion in three-dimensional space and extract anti-interference temporal optical flow features. In the second stage, a Transformer-based encoder is used to model and classify feature sequences by leveraging its capability for global feature modeling integrated with positional information. The experimental results on the CASME3-PartC and MEVIEW datasets show that the proposed method achieves F1-scores of 0.151 and 0.326, respectively, outperforming existing methods and achieving the current optimal performance. Cross-dataset experiments further validated the good generalization ability of this method. Full article

Three crosslinked polyurethane copolymers were successfully synthesized as polymeric solid–solid phase change materials (SSPCMs) for thermal energy storage. These materials were fabricated utilizing trihydroxy compounds (glycerol, triethanolamine, and trimethylolethane) as chain extenders and polyethylene glycol (PEG) as the phase change functional segment. A comprehensive suite of characterization techniques was employed to investigate the chemical structures, thermal properties, and crystalline behaviors of the resulting SSPCMs. Fourier transform infrared (FTIR) spectroscopy confirmed the successful synthesis of the crosslinked polyurethane network. Polarizing optical microscopy (POM) and wide-angle X-ray diffraction (WAXD) analyses revealed that all three SSPCMs exhibit regular spherulitic morphologies with sharp diffraction peaks resembling those of pure PEG, although variations in spherulite size and diffraction intensity were observed among the samples. Differential scanning calorimetry (DSC) demonstrated the reversible latent heat storage and release capabilities of the synthesized SSPCMs, with a maximum endothermic enthalpy (ΔH_endo) of 115.7 J/g. Furthermore, thermal cycling tests and thermogravimetric (TG) analysis verified their exhibit excellent reusability, thermal reliability, and high thermal stability. Full article

The rapid expansion of wireless communication in urban environments requires antenna systems that balance high electromagnetic performance with stringent aesthetic and security constraints. This review examines recent advances in concealed antenna technologies integrated into building structures, with a focus on performance variation, material-induced attenuation, and emerging concealment strategies. Techniques such as transparent conductors on glass, structural embedding within walls, and camouflage-based designs are shown to significantly influence resonance behavior, radiation efficiency, and pattern characteristics compared to free-space operation. Despite these challenges, optimized solutions including transparent conductive oxide arrays, wideband embedded antenna geometries, and metasurface-enhanced window structures can partially recover performance while maintaining optical transparency above 70%. Material loading effects are found to induce resonant frequency shifts of approximately 10–44%, depending on dielectric properties and environmental conditions. Transparent antenna arrays achieve gains ranging from 0.34 to 13.2 dBi, while signal-transmissive wall systems demonstrate transmission improvements of up to 22 dB relative to untreated building materials. These technologies enable a wide range of applications, including 5G and beyond-5G cellular networks across sub-6 GHz and millimeter-wave bands, as well as Internet of Things systems and smart city infrastructure. However, key challenges remain, including the need for comprehensive characterization of building material electromagnetic properties, optimization of multilayer structural environments, and the development of standardized design and evaluation methodologies. This review provides a unified framework for understanding the tradeoffs associated with antenna concealment and identifies critical research directions for the development of building-integrated wireless systems in next-generation communication networks. Full article

A multimodal strategy for fall detection within the broader domain of human activity recognition is developed in this study. A fine-tuned Inflated 3D Convolutional Network model, trained in optical flow data derived from video inputs, achieves 92.70% accuracy in classifying fall-related events. Simultaneously, a Convolutional Neural Network–Bidirectional Long Short-Term Memory model incorporating attention mechanisms processes time-series sensor data, contributing to an ensemble performance of 97.87%. The integration of visual and sensor modalities illustrates a promising direction for developing reliable, real-time fall detection systems applicable in healthcare and assisted living environments. Full article

Digital management of modern railway bridges, a substantial part of high-speed railway networks, is often hindered by manual interpretation of construction drawings for Building Information Modeling (BIM). While individual technologies like optical character recognition (OCR) and neural networks are well-established, their generic application often fails on complex engineering documents. To address this, a domain-adaptive automatic recognition and semantic interpretation framework is proposed for railway bridge construction drawings. The novelty of this work lies in a specialized hybrid data fusion strategy that intelligently merges vector CAD file parsing with morphology-denoised OCR, resolving spatial and semantic conflicts. Furthermore, a back-propagation (BP) neural network is explicitly adapted to classify the extracted text into specific engineering categories, overcoming the challenges of dense layouts and overlapping symbols. Finally, the framework achieves end-to-end integration by transforming these semantic entities directly into structured, IFC-compatible BIM parameters. Evaluated on 250 real-world drawings, the framework achieved an average F1-score of 91.0% in semantic classification and improved processing efficiency by 6.5 times compared to manual methods. Moreover, 93.8% of the extracted entities achieved strict BIM parameter correctness, defined as seamless mapping to Revit IFC attributes without manual intervention. Full article

Joint modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) monitoring constitutes one of the most critical functions integrated in digital coherent receivers, ensuring high flexibility and stability in elastic optical networks (EONs). Since signal amplitude information captures inherent characteristics associated with modulation formats and fluctuations induced by OSNR variations, a simple and effective optical performance monitoring (OPM) scheme based on an amplitude-analytic complex plane is proposed. By employing a multi-task learning algorithm incorporating the multi-order gated aggregation (MOGA) module, the proposed scheme enables simultaneous MFI and OSNR monitoring for polarization division multiplexed (PDM)-QPSK/-16QAM/-32QAM/-64QAM/-128QAM signals. The performance of the proposed scheme is numerically verified in 28 GBaud coherent optical communication systems of various configurations. Numerical simulation results show that 100% identification accuracy is obtainable for all five modulation formats, even at OSNR values lower than the corresponding theoretical 20% forward error correction (FEC) limit. Meanwhile, the mean absolute error (MAE) of OSNR monitoring for QPSK, 16QAM, 32QAM, 64QAM, and 128QAM are 0.16 dB, 0.15 dB, 0.17 dB, 0.28 dB, and 0.33 dB, respectively. Furthermore, simulation results show that the proposed scheme is robust to residual chromatic dispersion (CD) and the nonlinear effects with strong generalization capability. These results suggest that the proposed scheme is promising for applications in next-generation EONs. Full article

Fibre properties significantly influence paper quality. This study investigates fibre property development along an industrial pulp production line, analysing morphological distributions and rheological behaviour to enhance refining performance indicators. Understanding these developments is critical for optimising resource efficiency and increasing industrial sustainability. Softwood thermomechanical pulp (TMP), from high-consistency (HC) and low-consistency (LC) refining, and bleached hardwood kraft pulp (BHKP) were examined. Fibre morphological properties were characterised using an optical fibre analyser, while suspension rheology was assessed using a pulp viscometer, supported by computational fluid dynamics (CFD) and discrete element method (DEM) simulations. Results demonstrate that fibre property distributions provide deeper insights into refining effects compared to average values alone. Systematic trends showed that HC-refined TMP from the first and second refining stage required significantly greater torque to break the fibrous network and fluidise the pulp compared to pulp that was also LC refined. This indicates that alterations in fibre properties, especially shortened fibre length resulting from different refining processes, govern fibre interactions in the three-dimensional network of the pulp suspensions and, therefore, their flow behaviour. In conclusion, combining morphological distribution analysis with specialised rheological measurements offers a robust tool for better understanding and monitoring mill-scale refining processes, enabling improved process optimisation in pulping and papermaking. Full article