Search Results (652)

To enhance the gelling functionality of plant proteins, this study developed hybrid gels by blending casein with soy protein isolate (SPI) at various ratios using microbial transglutaminase (MTG) as a cross-linking catalyst. The gels were systematically characterized in terms of microstructure, water distribution, rheological and textural properties, secondary structure, and intermolecular interactions. Incorporation of casein significantly improved gel strength, water-holding capacity, and network uniformity. Notably, the 1:1 casein-to-SPI ratio yielded the highest performance, featuring extensive inter-protein cross-linking, an increased proportion of ordered secondary structures, and a finely porous matrix that effectively immobilized water. Mechanistically, MTG-catalyzed covalent bonding established the primary network scaffold, while hydrophobic interactions and disulfide bonds further stabilized the gel matrix. These findings demonstrate that MTG-induced Casein–SPI hybrid gels can enhance the functional properties of plant proteins and offer a viable strategy for designing sustainable protein-based food structures with tailored performance. Full article

In this work, compatibilized poly(lactic acid)/poly(butylene adipate-co-terephthalate) (PLA/PBAT) blends were developed and characterized, to be potentially utilized as biodegradable self-healing matrices for composite laminates. Blends containing 10, 20 and 30%wt of PBAT and 0.5 phr of an epoxy-based compatibilizer were prepared by melt compounding and hot pressing. Rheological measurements showed that moduli and complex viscosity generally increased with PBAT content, while maintaining viscosity levels suitable for conventional melt-processing operations. FT-IR and FESEM analyses confirmed the formation of an immiscible but well-compatibilized morphology, characterized by a homogeneous dispersion of PBAT domains within the PLA phase. Mechanical tests revealed a decrease in tensile modulus (up to 44%), strength (up to 45%) and fracture toughness (up to 40%) with a PBAT content up to 30%wt. Self-healing was evaluated by measuring the fracture toughness (K_IC) recovery after thermal treatment at 140 °C. After healing, the blend containing 20%wt of PBAT exhibited a self-healing efficiency of 64% under impact conditions, which was attributed to the smoother fracture surface generated at an elevated strain rate that facilitated a more effective flow of the molten PBAT phase across the crack interface during healing. The formulation containing 20%wt of PBAT featured the best balance between mechanical performance and self-healing efficiency. Full article

Background and Objectives: Diabetes mellitus represents one of the most prevalent chronic metabolic disorders worldwide, necessitating precise insulin dose management to prevent both acute and long-term complications. The optimization of insulin dosing remains a significant clinical challenge, as inappropriate dosing can lead to hypoglycemia or hyperglycemia, each carrying substantial morbidity risks. Machine learning approaches have emerged as promising tools for developing clinical decision support systems; however, their practical implementation requires both high predictive accuracy and model interpretability. This study aimed to develop and evaluate an explainable machine learning framework for predicting insulin dose adjustments in diabetic patients. We sought to compare multiple ensemble learning approaches and identify the optimal model configuration that balances predictive performance with clinical interpretability through comprehensive SHAP and LIME analyses. Materials and Methods: A comprehensive dataset comprising 10,000 patient records with 12 clinical and demographic features was utilized. We implemented and compared nine machine learning models, including gradient boosting variants (XGBoost, LightGBM, CatBoost, GradientBoosting), AdaBoost, and four ensemble strategies (Voting, Stacking, Blending, and Meta-Learning). Model interpretability was achieved through SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) analyses. Performance was evaluated using accuracy, weighted F1-score, area under the receiver operating characteristic curve (AUC-ROC), precision-recall AUC (PR-AUC), sensitivity, specificity, and cross-entropy loss. Results: The Meta-Learning Ensemble achieved superior performance across all evaluation metrics, attaining an accuracy of 81.35%, weighted F1-score of 0.8121, macro-averaged AUC-ROC of 0.9637, and PR-AUC of 0.9317. The model demonstrated exceptional sensitivity (86.61%) and specificity (91.79%), with particularly high performance in detecting dose reduction requirements (100% sensitivity for the ‘down’ class). SHAP analysis revealed insulin sensitivity, previous medications, sleep hours, weight, and body mass index as the most influential predictors across different insulin adjustment categories. The meta-model feature importance analysis indicated that LightGBM probability estimates contributed most significantly to the ensemble predictions. Conclusions: The proposed explainable Meta-Learning Ensemble framework demonstrates robust predictive capability for insulin dose adjustment recommendations while maintaining clinical interpretability. The integration of SHAP-based explanations facilitates clinician understanding of model predictions, supporting transparent and informed decision-making in diabetes management. This approach represents a significant advancement toward the clinical implementation of artificial intelligence in personalized insulin therapy. Full article

Development of biodegradable polymer composites provides a sustainable alternative to conventional plastics. This study systematically investigates the effect of Eucomis autumnalis (EA) cellulose on the morphological, structural, and thermal behavior of polybutylene succinate (PBS) and polycaprolactone (PCL) blends. EA cellulose was extracted via delignification and hemicellulose removal, yielding 38% cellulose from the leaf biomass. A series of PBS/PCL/EA cellulose composites were prepared using a solution-casting method. Fourier-transform infrared spectroscopy (FTIR) confirmed retention of characteristic functional groups, with spectra dominated by PCL features, indicating the absence of new chemical bond formation between EA cellulose and the polymer matrix. X-ray powder diffraction (XRPD) revealed that EA cellulose acted as a nucleating agent, enhancing the crystallinity, especially in PCL, while slightly affecting PBS crystallization. A scanning electron microscopy (SEM) analysis demonstrated preferential localization of EA cellulose within the PBS phase, contributing to improved phase dispersion and interfacial interaction at the morphological level. Differential scanning calorimetry (DSC) showed enhanced crystallization behavior of PCL at higher EA cellulose loading (5 wt.%), with minimal influence on PBS thermal transitions. A thermogravimetric analysis (TGA) indicated that the thermal stability depends on the polymer composition and cellulose content, with higher PCL fractions contributing to an improved stability. This study provides insight into the structure–property relationships governing PBS/PCL/EA cellulose systems and highlights the potential of EA cellulose as a bio-based additive for tailoring morphological and thermal characteristics of biodegradable polymer blends. A mechanical performance evaluation is recommended for future studies to correlate structural modifications with macroscopic properties. Full article

To ensure the viability of polymer materials, given the properties and limitations of polymers, hybrid materials have been developed that blend the features of all included components. Researchers have not explored the impacts of the length aspect ratio of nanofillers on the mechanical properties of hybrids in great detail previously. Multi-walled carbon nanotubes are a valuable option because they exhibit improved mechanical properties. Using numerical simulation, the impacts of nanofiller content and the size aspect ratio on two base materials—polyamide 6, polyoxymethylene—and their blends, were determined as a function of the volume ratio, the MWCNTs aspect ratio and the base material blend composition. Numerical analysis employed the ANSYS Material Designer. Random samples of chopped-fibre representative volume elements were generated, meshed and analysed by finite element analysis to obtain the Young’s modulus and Poisson’s ratio for each sample. The results showed a generally linear dependence. Rises in both aspect ratio and volume fraction of MWCNTs increased the Young’s modulus up to 46% and decreased the Poisson’s ratio up to 1.6%. The findings suggest that although the impact of the aspect ratio is not as large as that of the volume ratio, longer MWCNTs are preferable. Full article

In bulk heterojunction (BHJ) organic solar cells (OSCs) employing perylene diimide (PDI)-based non-fullerene acceptors, excessive intermolecular interactions among PDI units lead to severe aggregation and pronounced donor–acceptor phase separation, both of which critically limit device performance. To address these issues, numerous structurally engineered PDI derivatives have been developed. In particular, twisted multi-PDI architectures designed to suppress intermolecular aggregation have shown improved morphological control; however, such twisted structures are often highly amorphous, which reduces electron-transport efficiency and constrains OSC performance. In this work, we introduce a mixed-acceptor strategy combining a twisted PDI dimer (SF-PDI₂) with a planar monomeric PDI (m-PDI) to balance aggregation and morphological uniformity. Ternary blend OSCs consisting of PTB7-Th as the donor and these two PDI acceptors exhibit systematic performance variations depending on their relative ratios. At the optimized composition (SF-PDI₂:m-PDI = 90:10 by weight), the device outperforms single-acceptor systems, which is attributed to controlled aggregation arising from the complementary structural features of the two PDI acceptors. This study demonstrates that combining mixed PDI acceptors with similar molecular moieties enables precise control of aggregation, improving both morphology and photovoltaic performance. Full article

Unmanned aerial vehicle (UAV) image stitching aims to generate panoramic remote sensing images beyond the field of view of a single camera. However, there are still significant challenges in constructing panoramic images of a target area quickly and accurately, especially in terms of computationally intensive feature matching extraction and feature alignment accuracy, which are particularly sensitive to high-resolution and low-texture scenes. To address this problem, this study proposes an efficient image stitching method that incorporates dense depth kernelized feature extraction and geometric constraint optimization. The learning-based kernelized feature matching framework is adopted to achieve subpixel-level dense matching, which effectively overcomes the time-consuming and sparse matching deficiencies of traditional manual features (e.g., SIFT) in high-resolution images. Second, a two-layer geometrically constrained mismatching filtering strategy is designed, which significantly improves the alignment accuracy in low-texture and large-parallax scenarios. Finally, panoramic stitching is achieved through a hybrid strategy consisting of a single-responsive transform and max-intensity pixel blending strategy to realize panoramic stitching. Experimental results obtained on multiple datasets show that the proposed method achieves similar visual quality metrics (PSNR, SSIM, and LPIPS) while reducing the stitching time to just 17.5% of that of the baseline method. This makes it a practical solution for efficiently stitching large UAV images. Full article

This study combines multi-frequency dynamic mechanical analysis (DMA) with machine learning (ML) to characterize and predict the viscoelastic properties of a commercial polycarbonate/acrylonitrile–butadiene–styrene (PC/ABS) blend. DMA temperature sweeps at four frequencies (1–10 Hz) in single cantilever mode yielded a glass transition range of 115.8–$123.2$ °C ($E^{″}$ peak), frequency sensitivity of $7.18$ °C/$\mathrm{decade}$, and an apparent activation energy of $335\pm 85 \mathrm{k}\mathrm{J} {\mathrm{mol}}^{-1}$. Time–temperature superposition master curves were parameterized with a six-term Prony series ($R^2=0.998$). Four data-driven models (RF, XGB, SVR, MLP) and a physics-informed NeuralWLF model were evaluated through a hierarchical validation framework. Temperature-blocked CV ranked MLP ($\overline{R^2}=0.989$) above RF (0.950) for interpolation; LOFO validation revealed that NeuralWLF achieved the best cross-frequency generalization ($R^2>0.92$ for all targets) with interpretable WLF parameters ($C_1\approx 12.2$, $C_2\approx 51.7 °\mathrm{C}$). A systematic block size sweep (5–30 $°\mathrm{C}$) revealed a validation inflation effect in which MLP $tan\delta$$R^2$ dropped from 0.986 to 0.592 as the gap-to-FWHM ratio increased from 0.5 to 3.1, establishing the gap/FWHM ratio as a quantitative validation stringency criterion. A physics–data crossover was identified at gap/FWHM $\approx 2$: beyond this threshold, NeuralWLF outperformed all data-driven models in $tan\delta$ prediction by up to $+0.300$ in $R^2$, while curriculum learning (freezing the WLF layer for 300 epochs) further improved the most stringent 30 $°\mathrm{C}$ validation from $R^2=0.660$ to 0.731. The integrated framework demonstrates that honest evaluation of DMA–ML models requires validation gaps exceeding the characteristic feature width and introduces a quantifiable physics-data crossover criterion for selecting between data-driven and physics-informed architectures. Full article

A method for simultaneous classification of fabric material and color based on hyperspectral imaging and visual detection is proposed. Fabric material classification is performed using hyperspectral imaging (HSI) combined with a one-dimensional convolutional neural network (1D-CNN), while fabric color recognition is achieved using an red-green-blue (RGB) camera and a color classification model. Material and color features from the same fabric sample are matched to realize synchronous classification. Experiments were conducted on three fabric materials (cotton, polyester, and cotton–polyester blend) and eight colors. At a conveyor speed of 1 m/s, the sorting success rates reach 95.0% for cotton, 97.5% for polyester, and 85.0% for cotton–polyester blended fabrics. The proposed method demonstrates reliable performance for single-material fabrics and good industrial applicability for automated fabric sorting. Full article

The incorporation of slag and calcium carbonate (CaCO₃) as clinker-reducing constituents offers significant potential for lowering CO₂ emissions in cement production; however, their combined influence on age-dependent compressive strength remains complex and highly coupled. In this study, a structured literature-based dataset ($N=75$ mix conditions) was compiled from two independent experimental sources to investigate compressive strength development in slag–CaCO₃ blended cementitious systems. Compressive strength at 3 and 28 days was formulated as a multi-output regression problem to explicitly capture the correlated nature of strength evolution between early-age and later-age curing stages. Dataset-level analysis revealed that CaCO₃ replacement exerts a stronger influence on early-age strength (reductions of approximately 15–25%) than on later-age strength (typically within 5–15%), indicating a transition from clinker-dominated hydration to slag-controlled later-age strength development. Compared with independent single-output models, the proposed multi-output framework improved prediction performance by increasing $R^2$ values by approximately 4–6% and reducing RMSE by up to 15–18%. Feature importance analysis identified slag replacement ratio and CaCO₃ dosage as the dominant predictors, while chemical composition descriptors modulated age-dependent sensitivity. The results demonstrate that compressive strength at different curing ages is governed by coupled yet temporally evolving physicochemical mechanisms. From an engineering perspective, CaCO₃ replacement should be evaluated within an integrated compositional design framework that considers curing-age requirements and slag reactivity. Overall, this study provides a transparent and statistically robust approach for analyzing strength evolution in blended cement systems and highlights the value of multi-output learning for age-dependent performance prediction in sustainable cementitious materials. Full article

Widening participation policy in England is increasingly collaborative. Since 2018, higher education (HE) institutions charging above the basic tuition fee limit are required to set out strategies to mitigate ‘risks to equality of opportunity’ for people from more disadvantaged backgrounds and their ability to access and progress through and from higher education’. Universities are encouraged to work with schools to implement outreach initiatives such as supporting raising attainment—stimulating prospects for strategic collaboration and leadership across organisational boundaries. While the majority of leadership studies in the educational research literature showcase individual settings or sectors, our study of a schools–university partnership investigates collaborative leadership practices across institutional and sector borders. Drawing ethnomethodological insights from rich qualitative data compiled 15 months into the partnership—comprising semi-structured interviews with school leaders and teachers, meeting observations, and researcher field notes—we present a unique school stakeholders’ perspective of a boundary-spanning partnership focused on university outreach and educational improvement. Venturing across institutional borders revealed pathways to develop more diffuse forms of coordinated action around a common goal—activating increased leadership-based collaboration and creativity among school stakeholders alongside a need for greater shared understanding to avoid potential misalignments. Facilitated by ‘knowledge brokering’ between school and university stakeholders, features of collaborative leadership manifested as a blended phenomenon—with teachers and leaders signalling pragmatic shifts in attainment-raising framing and practice. Implications for both schools and HE sectors are offered, distinctively at the intersection of school leadership and widening participation. Full article

This study presents a real-time framework for generating two-dimensional (2D) orthomosaic maps directly from UAV video. The method targets operational scenarios in which a continuously updated 2D overview is required during flight or immediately after landing, without relying on time-consuming offline photogrammetry workflows such as structure-from-motion (SfM) and multi-view stereo (MVS). The proposed procedure incrementally registers sparsely sampled video frames on standard CPU hardware using classical feature-based image registration. Each selected frame is converted to grayscale and processed under a fixed keypoint budget to maintain predictable runtime. Tentative correspondences are obtained through descriptor matching with ratio-test filtering, and outliers are removed using random sample consensus (RANSAC) to ensure geometric consistency. Inter-frame motion is modeled by a planar homography, enabling the mapping process to jointly account for rotation, scale variation, skew, and translation that commonly occur in UAV video due to yaw maneuvers, mild altitude variation, and platform motion. Sequential homographies are accumulated to warp incoming frames into a global mosaic canvas, which is updated incrementally using lightweight blending suitable for real-time visualization. Experimental results on three UAV video sequences with different durations, flight patterns, and scene targets report representative orthomosaic-style outputs and per-step CPU runtime statistics (mean, 95th percentile, and maximum), illustrating typical operating behavior under the tested settings. The framework produces visually coherent orthomosaic-style maps in real time for approximately planar scenes with sufficient overlap and texture, while clarifying practical failure modes under weak texture, motion blur, and strong parallax. Limitations include potential drift over long sequences and the absence of ground-truth references for absolute registration-error evaluation. Full article

Despite the enormous amounts of waste textiles produced by the world’s textile industry’s explosive growth, resource utilization rates are still poor. Cotton/polyester blended waste fabrics make up a sizable share, and sorting them precisely is essential to increasing recycling value and promoting the circular economy in the textile industry. Traditional mechanical and human sorting techniques are ineffective and inaccurate; current spectral analysis algorithms mainly concentrate on quantitative composition prediction and are insufficiently capable of differentiating between waste fabrics with comparable content gradients. To address these challenges, this paper proposes an improved 1DCNN model (Dual-1DCNN-Residual-SE) integrated with Near-Infrared (NIR) hyperspectral imaging technology. This model takes raw spectral data and Savitzky-Golay (SG) smoothing data as dual-channel inputs, introducing residual connections to capture subtle spectral differences between similar fabric categories, and employs SE attention mechanisms to adaptively enhance key features. Comparative experiments with four traditional algorithms—KNN, RF, SVM, and PLS—demonstrate that the proposed model achieves a classification accuracy of 95.94%, surpassing the best traditional algorithm SVM (88.12%) by 7.82%. Ablation experiments confirm each enhanced module’s efficacy. This study achieves high-precision classification of cotton/polyester blended waste fabrics, providing technical support for intelligent sorting of industrial waste fabrics. Full article

Natural polyphenols, particularly quercetin (QUE) and rosmarinic Acid (RA), possess significant synergistic therapeutic potential as potent antioxidants and anti-inflammatories. However, their poor stability, low water solubility, and resulting limited bioavailability severely hinder their effective clinical translation. This study addresses these fundamental limitations by designing a novel advanced drug delivery platform utilizing electrospinning. We have fabricated composite high-molecular-weight poly(L-Lactic Acid) (PLA)/polyethylene glycol (PEG) nanofibers for the simultaneous co-delivery of both QUE and RA, optimizing compound stability and release kinetics. PLA provided mechanical integrity and sustained release properties, while the incorporation of PEG strategically enhanced the mat’s wettability, enabling precise control over initial drug dissolution. Comprehensive characterization confirmed uniform, bead-free morphology and high entrapment efficiency for both polyphenols. Crucially, the PLA/PEG blend successfully achieved a biphasic release profile, featuring an initial burst release mediated by PEG followed by a sustained release phase governed by the PLA matrix. Furthermore, the performed in vitro investigations using SH-4 melanoma cells and HaCaT normal keratinocytes revealed that the prepared novel materials containing the polyphenols possessed high anticancer activity to the used cancer cell line. However, the toxicity to the normal cell line is much lower. Therefore, this novel electrospun composite scaffold offers an effective strategy to enhance the stability, control the delivery, and maximize the synergistic therapeutic benefits of quercetin and rosmarinic Acid for applications in areas such as advanced wound care, tissue regeneration, and antitumor therapies. Full article

The accuracy of forest canopy height estimation is crucial for forest resource management and ecosystem carbon sequestration. However, existing approaches often face limitations in effectively integrating multisource remote sensing data, feature representation, and model learning strategies. To enhance the prediction performance of the model in complex terrain and multisource data environments, this study comprehensively used ICESat-2/ATLAS photon point clouds, Sentinel-2/MSI multispectral imagery, and SRTM-DEM to construct a remote sensing-driven multisource feature system, which eliminated redundant interference using permutation feature importance analysis. Additionally, a self-attention (SA) mechanism was introduced to strengthen high-dimensional feature representation. Three heterogeneous models, incorporating deep neural network (DNN), extreme gradient boosting (XGBoost), and residual network (ResNet), were independently applied for forest canopy height estimation and were further used as base learners, with a random forest as the meta-learner, and an SA-Blending heterogeneous ensemble model that combines a blending technique with an SA mechanism was proposed to enhance the accuracy of forest canopy height estimation. To evaluate the SA optimization strategy and the role of multisource fusion, this study used the original features, SA-optimized features, and multisource fusion features (i.e., the concatenation and fusion of original features and self-attention mechanism features) as inputs to comprehensively compare the performance of each single model and the integrated model. The results show that: (1) The self-attention mechanism significantly improves the prediction performance of heterogeneous models. Compared with original features inputs, the R² of DNN (SA-Only) and XGBoost (SA-Only) increased to 0.706 and 0.708, respectively, and the RMSE decreased to 1.691 m and 1.613 m. Although the R² for ResNet (SA-Only) decreased slightly to 0.699 and the RMSE increased to 1.712 m, the overall impact was not significant. (2) Under the condition of multisource fusion feature input, DNN+SA, XGBoost+SA, and ResNet+SA all demonstrated higher fitting accuracy and stability, verifying the enhancing effect of the SA mechanism on the association expression of multisource information. (3) The SA-Blending model achieved the best overall performance, with R² of 0.766 and RMSE of 1.510 m. It outperformed individual models and the SA-optimized model in terms of overall accuracy, stability, and robustness. The results can provide technical support for high-precision forest canopy height mapping and are of great significance for ecological monitoring applications. Full article