Search Results (2,620)

Driven by the growing demand for functional textiles featuring excellent waterproofness, moisture permeability and mechanical robustness in outdoor sportswear, medical protection and technical apparel, traditional pongee—despite its desirable softness, high wrinkle resistance and good stability as an ideal substrate fabric—is severely restricted in further application by its intrinsically poor hydrostatic pressure resistance in extremely wet environments. Accordingly, we developed a modified waterborne polyurethane (WPU) coating for pongee substrates to fabricate functional textiles that maintain high hydrostatic pressure resistance while possessing good mechanical properties and increased UV absorption. In this study, by using the sol–gel method, an amorphous silicon dioxide (SiO₂) coating layer was constructed on the surface of titanium dioxide (TiO₂) particles, forming silica-modified titania particles (SiO₂/TiO₂). These SiO₂-modified particles were subsequently physically blended with an anionic waterborne polyurethane system that had been previously modified with a polyester-type modifier A to enhance its hydrostatic pressure resistance. The resulting composite coating was designed to combine the high hydrostatic pressure resistance inherited from the modified WPU matrix, the mechanical reinforcement and increased UV absorption contributed by SiO₂/TiO₂, and satisfactory water repellency on fabric substrates. The results indicate that the incorporation of an appropriate amount of modifier A into the prepolymer system significantly enhances hydrostatic pressure resistance while maintaining high elongation at break. At a SiO₂/TiO₂ loading of 0.2 wt%, the composite film exhibits optimal comprehensive performance, characterized by superior mechanical properties, low water absorption, and static water contact angles exceeding 100° for coated fabrics. SiO₂/TiO₂ composite WPU coatings substantially improve hydrostatic pressure resistance across various fabrics, with 380T polyester taffeta demonstrating the best performance. This resistance remains remarkably stable after standard washing, indicating excellent wash fastness and practical applicability. Full article

Accurate forecasting of urban water demand is essential for scientific regulation and sustainable management of water resources, particularly in complex DMA (District Metered Area) environments. This study proposes an integrated regional water demand prediction framework that combines CEEMDAN decomposition with deep learning techniques. CEEMDAN is first applied to decompose the original water demand time series into multiple Intrinsic Mode Functions (IMFs), effectively extracting multi-scale features and mitigating non-stationarity and complexity. A hybrid Transformer-BiLSTM model is then constructed to capture global dependencies, nonlinear dynamics, and bidirectional temporal features. Experimental results demonstrate that the proposed CEEMDAN-Transformer-BiLSTM model significantly outperforms various benchmark models in terms of prediction accuracy, robustness, and generalization across different DMAs. This research provides a new perspective for modeling complex water resource time series and offers theoretical and practical support for optimizing urban water allocation and achieving sustainable management, while laying a foundation for future work involving external driving factors, enhanced model interpretability, and dynamic regulation mechanisms. Full article

Programmable peptide hydrogels represent advanced supramolecular biomaterials featured with customizable molecular sequences and tunable self-assembly behaviors, which can biomimetically reconstruct the structural and microenvironmental complexity of native extracellular matrix. This review systematically elaborates the molecular engineering advances of programmable peptide hydrogels following a hierarchical logic from fundamental mechanisms to translational applications. We first interpret the intrinsic self-assembly mechanisms driven by non-covalent interactions and the regulatory effects of typical external microenvironmental stimuli. On this basis, we summarize core rational design principles, covering stimuli-responsive structural optimization, biofunctional modification, and the tunable regulation of physical properties, degradability and immunogenicity. Furthermore, we correlate multi-scale structural features (nanostructures, porous architecture and mechanical properties) with their versatile biomedical functions, and comprehensively discuss their cutting-edge applications in tissue regeneration, targeted drug and gene delivery, cell-mediated therapy, immunomodulation, and anti-infective treatment. Finally, we identify critical translational barriers including batch-to-batch inconsistency, immunogenic risks, and in vivo performance instability, and highlight future directions involving multi-stimuli-responsive systems, artificial intelligence-assisted design, computational modeling, and hybrid material construction. This work systematically clarifies the structure–property–function relationship of peptide hydrogels, and underscores their great potential as next-generation platforms for precision regenerative medicine and targeted disease intervention. Full article

Microbial classification and taxonomic information are fundamental to microbiological studies. Raman microspectroscopy, a rapid and non-destructive single-cell analytical technique, captures intrinsic molecular fingerprints reflecting cellular biochemical composition, thereby enabling microbial classification at the single-cell level. However, current Raman-based classification frameworks allow accurate identification only for micro-organisms already represented in reference databases. These approaches often fail or yield errors for uncharacterized microorganisms. To address this limitation, we collected 6600 single-cell Raman spectra from 11 microbial species, including bacteria and fungi, and developed deep learning models for rapid classification. A hierarchical clustering (HC) framework based on Raman features extracted by a one-dimensional convolutional neural network (1D-CNN) was constructed and compared with phylogenetic trees derived from rRNA gene sequences. 1D-CNN achieved high classification performance with an overall accuracy of 99.7%. Notably, the Raman HC tree exhibited clear concordance with phylogenetic structures, particularly at the higher taxonomic levels. Validation using five independent unknown strains demonstrated that the Raman HC tree consistently positioned these strains near their closest phylogenetic relatives, in strong agreement with sequence-based analyses. Collectively, these findings highlight the potential of single-cell Raman spectroscopy with deep learning as an alternative and complementary framework for microbial taxonomic analysis, particularly for previously uncharacterized microorganisms. Full article

The foot structure plays a decisive role in the trafficability of legged robots on granular media. Traditional foot-ends (spherical, cylindrical, flat-bottomed) are prone to sinkage and slippage, resulting in unstable locomotion. To solve this problem, a novel bionic-inspired reconfigurable foot with active opening and closing adjustment capability is designed based on bionics, combining the stable phalangeal contour of goat hoof capsules and the high-adhesion feature of beetle foot-end spines. A coupled EDEM–Adams simulation model is established, and physical experiments combined with simulation inversion are used to calibrate contact parameters between particles and between particles and the foot, including the coefficient of restitution, static friction and rolling friction. A high-fidelity numerical platform for foot–ground dynamic interaction is thus constructed. By comparing and analyzing the differences in anti-sinkage and traction performance between the bionic-inspired foot and traditional foot-ends, this study systematically revealed the influence law of bionic morphology on the mechanical behavior of the foot, and clarified the intrinsic mechanism through which bionic design improves foot–ground interaction. The results demonstrate that the spine structures of the bionic-inspired foot reshape the mechanical constitutive relationship of granular media. By expanding the ground contact area and optimizing contact pressure distribution, the maximum reduction in foot sinkage depth reaches 70.11%, and the traction coefficient is increased by up to 37.13%. Full article

In this study, we utilize the ${\varphi }^6$-model expansion method to derive a diverse set of Jacobi elliptic function solutions for the conformable resonant Nonlinear Schrödinger Equation (NLSE) with parabolic law nonlinearity. As the modulus of the Jacobi elliptic functions approaches 1 and 0, the solutions transform into hyperbolic and trigonometric functions, respectively. This methodology yields various exact traveling wave solutions, including kink solitons, singular solitons, periodic solutions, and singular periodic solutions. Notably, this work represents the first investigation into identifying Jacobi elliptic function solutions for the conformable resonant NLSE. These results enhance the understanding of the nonlinear dynamical properties intrinsic to the NLSE. We use graphical illustrations to highlight the dynamical features of the solutions. Moreover, our approach showcases versatility in addressing other nonlinear partial differential equations, offering insights applicable to nonlinear optics, fluid dynamics, and quantum physics. Full article

To address the global water crisis, desalination technologies contribute about 1% of the global freshwater supply. Membrane-based desalination technologies offer high performance, operational ease, cost-effectiveness and high scalability compared to conventional thermal desalination modes. Among all membrane-based technologies, reverse osmosis is prevailing globally. However, the high energy demand of the reverse osmosis process and fouling in case of hypersaline feed streams motivate the exploration of alternative technologies, i.e., pervaporation. Pervaporation desalination involves dense hydrophilic polymer membranes to deal with high salt streams at low cost, along with less fouling than a few other membrane processes, i.e., reverse osmosis and membrane distillation. Mass transport through pervaporation desalination membranes is well-explained by solution-diffusion theory involving a tri-stage transfer, i.e., sorption, diffusion and evaporation. Since the last few decades, a green approach in all domains has offered chemical products and processes with the least hazards and minimal waste production. Application of biodegradable materials like poly(lactic acid) in combination with suitable green solvents, e.g., ethyl lactate, methyl lactate, cyrene, dimethyl isosorbide and gamma valerolactone for pervaporation desalination would be a good roadmap to meet the sustainability criterion. Some intrinsic features of poly(lactic acid) that make it a ‘material of choice’ for pervaporation desalination include hydrophilicity imparted by the presence of polar ester groups, high salt rejection, biodegradability with simple mineralization products, i.e., H₂O and CO₂, sustainable production, low toxicity, low carbon footprint, ease of processing and versatility. Poly(lactic acid) undergoes four interrelated degradation mechanisms: hydrolytic degradation, biodegradation, thermal degradation and photodegradation. The concern for poly(lactic acid) based pervaporation desalination is increased hydrolytic cleavage of poly(lactic acid) at high temperatures, which requires some modifications, e.g., nanoenhancement, additions of crosslinkers, surface modifications, addition of other polymers to prepare blends and post-treatments. These modifying strategies result in an increased stability and better performance of poly(lactic acid) films. However, optimization of various parameters relevant to such modifications leaves room for further research. This review offers a critical analysis of the need for biodegradable polymers with special focus on poly(lactic acid) rather than their fossil fuel-based alternatives, the environmental and health effects of all these polymers, cost estimation and possible performance-efficient, green and eco-friendly solutions. Full article

Phosphate waste rock piles (PWRPs) generated by open-pit phosphate mining are highly heterogeneous and difficult to characterize using conventional point sampling alone, which limits representative resource assessment, selective recovery, and rehabilitation planning. This study develops an integrated framework combining PRISMA spaceborne hyperspectral imagery, ground-based mineralogical analyses, and spatially constrained machine learning to map lithological heterogeneity at the Benguerir phosphate mining site, Morocco. A three-stage spectral optimization workflow, including atmospheric band masking, Savitzky–Golay filtering, and analysis of variance (ANOVA)-based feature selection, was applied to identify the most discriminative Short-Wave Infrared (SWIR) bands for lithological classification. After removing redundant observations located within shared PRISMA pixel footprints, 127 spatially independent samples were retained for model development. Five supervised classifiers (Random Forest, Extra Trees, XGBoost, Support Vector Machine, and K-Nearest Neighbors) were evaluated under a spatially constrained cross-validation framework aligned with the 30 m native PRISMA pixel size. Ensemble-based models, especially Extra Trees and Random Forest, provided the most stable performance, with balanced accuracies of 0.56–0.69 and area under the receiver operating characteristic curve (AUC) values exceeding 0.95 for carbonate-dominated lithologies. Lower discrimination between phosphate and siliceous facies reflects intrinsic mineralogical mixing and spectral overlap at the sensor scale. Entropy-based uncertainty and posterior probability mapping revealed spatially structured prediction ambiguity concentrated along lithological boundaries and transitional zones, consistent with petrographic evidence of compositional heterogeneity. These results indicate that moderate but stable accuracies likely represent realistic performance limits for spaceborne hyperspectral mapping of complex mining environments under spatial constraints. The proposed framework provides a transferable and uncertainty-aware basis for lithological mapping, selective recovery assessment, and sustainable phosphate waste management. Full article

Synthetic Aperture Radar (SAR) has broad application prospects in target recognition; however, intrinsic multiplicative speckle noise, geometric distortions, and the complex coupling of multimodal features often limit the comprehensiveness of representations and the efficiency of fusion, thereby restricting recognition accuracy. To address these limitations, this paper proposes a SAR image target recognition method based on multidimensional feature fusion. The proposed method first achieves noise suppression and contrast enhancement through an optimized preprocessing layer. Subsequently, a dual-branch hierarchical feature extraction network synchronously captures low-dimensional physical prior features driven by domain knowledge and highly discriminative deep convolutional features, ensuring a balance between physical interpretability and high-capacity representation. Finally, a variance-adaptive weighted fusion layer dynamically balances the contribution of different feature streams, mitigating information redundancy and feature conflict. Quantitative experiments on the MSTAR and public CETC38-SAR datasets demonstrate that under various pre-trained backbones, the proposed framework improves precision, recall, and F1-score by 5%–15% compared with baseline methods. Ablation studies and evaluations under extended operating conditions further validate the robustness, computational efficiency, and structural validity of the decoupled architecture. Full article

Arthrogryposis multiplex congenita (AMC) is a diverse group of conditions characterized by multiple joint contractures. Although individually rare, these disorders are estimated to affect 1 in 3000–5000 live births. Their common pathophysiological mechanism is fetal akinesia, a sustained reduction of fetal movement that may arise from intrinsic disturbances—such as central nervous system malformations, motor neuronopathies, neuropathies, neuromuscular junction defects, congenital myopathies, muscular dystrophies, or metabolic diseases—or from extrinsic factors including uterine constraint, maternal illness, infections, or toxic exposures. Reduced fetal motion leads to relatively uniform clinical manifestations, known as the fetal akinesia deformation sequence (FADS), which is characterized by craniofacial anomalies, pulmonary hypoplasia, growth restriction, and contractures. Currently, AMC is classified by clinical features, such as distal arthrogryposis or lethal congenital contracture syndromes. However, advances in molecular genetics have shown wide variability among conditions classified into the same category. Prognosis is widely variable, ranging from lethal perinatal forms to non-progressive mild conditions. This review discusses AMC etiologies from a topographic standpoint, considering the different levels of the motor system involved, by combining current clinical, genetic, and pathophysiological information. Full article

This study presents a comprehensive first-principles investigation of the structural, elastic, electronic, and optical behavior of cubic CsCaF₃ under hydrostatic pressure. The material is confirmed to be a stable Pm-3m fluoride perovskite, with a lattice constant of $4.496 \AA$ and a tolerance factor of $0.902$. At ambient conditions, CsCaF₃ exhibits high intrinsic stiffness ($C_{11}=107.88 \mathrm{G}\mathrm{P}\mathrm{a}$, $B=53.07 \mathrm{G}\mathrm{P}\mathrm{a}$, $G=29.16 \mathrm{G}\mathrm{P}\mathrm{a}$, $E=73.94 \mathrm{G}\mathrm{P}\mathrm{a}$) and maintains mechanical stability while becoming progressively stiffer under compression. The electronic structure reveals a wide indirect band gap of $7.1 \mathrm{e}\mathrm{V}$ that broadens to $8.43 \mathrm{e}\mathrm{V}$ and transforms into a direct gap at elevated pressures. Optical calculations show strong transparency in the visible range, with a low refractive index ($1.58$) and reflectivity ($~5\%$), and a deep-UV absorption edge near $6 \mathrm{e}\mathrm{V}$. Pressure enhances these features, increasing the refractive index to 1.66 and the maximum reflectivity to 45.87% at $24 \mathrm{G}\mathrm{P}\mathrm{a}$. The plasmon resonance also displays pronounced tunability, blue-shifting from $29.56$ to $30.79 \mathrm{e}\mathrm{V}$ with a fourfold rise in intensity. Analysis of the effective-electron number further indicates pressure-driven redistribution of spectral weight within the UV region. Together, these findings demonstrate that CsCaF₃ combines robust structural stability with highly pressure-tunable optical and plasmonic responses, positioning it as a promising candidate for deep-UV optoelectronics, photonic coatings, and pressure-responsive optical technologies. Full article

Robot control based on visual information perception has been a hot topic in the field of industrial robots, and the use of visual servoing technology to guide robots for high-precision spatial localization of machined workpieces has a wide range of application value. Aiming at the camera hand–eye calibration error and robot repositioning error, which have a large impact on the spatial localization and navigation accuracy, and when the binocular camera Z-direction accuracy is not high enough and the viewing angle is limited, etc., we propose a spatial visual servoing algorithm based on an orthogonal vision system that combines an eye-in-hand camera and an eye-to-hand camera in a hybrid configuration. By extracting sub-pixel image features in real time and deriving directionally decoupled interaction matrices, a linear controller is designed to guide the robot in the XY-plane and Z-direction separately. This decoupling strategy enlarges the convergence domain, avoids local minima caused by coupled degrees of freedom, and enhances system stability. To this end, the intrinsic calibration and hand–eye calibration of two cameras placed orthogonally are carried out firstly, and the accuracy of hand–eye calibration is not too demanding; then the sub-pixel level image position of the target is extracted in real time and the interaction matrix is derived and a linear controller is designed to control the robot’s motion; finally, the experiments of spatial localization accuracy are completed on the KUKA iiwa to validate the effectiveness of the method. Full article

Serverless computing has emerged as a prominent research focus in cloud computing because it provides infrastructure-transparent development and elastic resource management. However, this computing paradigm still faces the inherent challenge of cold start. Existing approaches have two major limitations: insufficient workload prediction accuracy and inefficient allocation of reusable container replicas to incoming function requests. To address these challenges, we propose a container scheduling approach based on Workload Prediction and Particle Swarm Optimization (PSO), named WPPSO. WPPSO first leverages a code-pre-trained large language model (LLM) to extract intrinsic function features and then uses a spatio-temporal fusion-based temporal neural network (STF-TNN) to predict serverless workloads. It subsequently employs a greedy algorithm to construct a high-quality initial matching state and uses PSO to refine the container scheduling strategy. Finally, WPPSO introduces a hierarchical container recycling mechanism to reduce idle resource waste. Extensive experiments show that WPPSO reduces startup latency by up to 72.2% and memory footprint by 63.4% compared with the native Knative platform. Compared with RainbowCake, WPPSO achieves a 15.6% lower mean startup latency without statistical significance and a statistically significant 31% reduction in idle memory consumption. Full article

High-precision cloud detection is essential for remote sensing applications such as agricultural monitoring and disaster response. However, thin clouds severely limit detection accuracy. The difficulty lies in their semi-transparent nature, which causes their reflected signals to couple with the reflectance of various underlying surfaces. This coupling leads to inconsistent cloud signatures and significant intra-class variability. To address this, we propose a Class Center Residual Attention Network (CCRANet), a radiative transfer theory-inspired framework that employs a class center approach to extract the intrinsic reflective characteristics of thin clouds. Specifically, the core of the network is the Class Center Attention (CCA) module, which extracts invariant intrinsic features of thin clouds, supplemented by the Class Center Residual (CCR) module to eliminate surface-induced interference. Experiments on three public datasets (Landsat-8, CSWV, and CloudS26) show that CCRANet achieves a mean Intersection over Union (mIoU) of 85.93% on the Landsat-8 dataset, outperforming the classic DeeplabV3+ baseline by 10.23 percentage points. In particular, it achieves 22.58 percentage point improvement in thin cloud IoU over DeeplabV3+ in snow/ice scenarios, significantly reducing false positive detections caused by surface spectral similarity. Full article

Passive millimeter-wave (PMMW) imaging technology has become a highly promising technology that can protect privacy in human body security inspections. However, most existing methods rely on single-pixel and single-polarization processing mechanisms, which often lead to discrete false-alarm pixels or missed detections in practical applications. Although multi-polarization information can provide richer distinguishing features, the current methods typically depend on limited Stokes parameters or artificially designed polarization features, lacking a systematic framework to fully exploit the intrinsic potential of multi-polarization information. In this paper, we propose a novel multi-scale edge-preserving decomposition model, termed Gaussian and weighted average curvature filtering (GWACF), to hierarchically decompose a multi-polarization PMMW image into three structural layers: base structural (BS) layer, coarse structural (CS) layer, and fine structural (FS) layer. Furthermore, we also propose a fusion strategy in which a gradient-domain pulse-coupled neural network (PCNN) is employed to fuse the texture-rich CS and FS layers, while the energy attribute fusion method is applied to the BS layer where primary structure and background information play a dominant role. This method effectively leverages complementary polarimetric information without introducing artifacts or compromising edge sharpness. Experimental results demonstrate that the proposed method effectively enhances the brightness temperature (BT) contrast of concealed objects. Compared with existing mainstream methods, it exhibits notable advantages in both detection accuracy and robustness. Full article