Search Results (894)

Aluminum alloy has been increasingly widely used in the construction field due to its green advantages of light weight, easy processability, high corrosion resistance, and recyclability, which conforms to the concept of green energy conservation and sustainable development in modern architecture. To improve its performance, carbon fiber-reinforced polymer (CFRP) was used to reinforce aluminum alloy pipes. A total of 22 groups of specimens with different lengths, thicknesses, and CFRP configurations were constructed to study their mechanical properties under axial compression. The experimental results show that CFRP reinforcement can effectively inhibit the lateral deformation and delay the global buckling of aluminum alloy pipes, among which the three-segment and full-coverage reinforcement have significant effects; the combination of aluminum and CFRP can transform direct failure into progressive failure and improve bearing capacity. This composite material not only has an excellent high strength-to-weight ratio and durability, but also can reduce structural self-weight. Full article

The bonding between cabin sections and exterior shells represents a critical manufacturing operation in shell assembly, directly determining the reliability and structural performance of the assembled structure. However, traditional manual scraping suffers from low efficiency, poor consistency, and heavy reliance on manual operation, while conventional teach-and-repeat robotic automation fails to adapt to significant manufacturing tolerances and complex surface curvatures common in large-scale shell components. To address these challenges, this paper proposes a vision-guided robotic scraping method that generates adaptive trajectories on irregular cabin sections. The method achieves full pipeline integration and is particularly suited for production lines where various models share similar macro-geometries but possess subtle geometric variations. A system integrating a laser profile sensor is developed to perceive surface geometry and local normal vectors. By establishing a unified coordinate transformation chain and a scan–mesh–spline workflow, the sensed geometric information is directly mapped to the robot end-effector pose. A trajectory generation algorithm based on point cloud meshing and B-spline interpolation is employed to construct continuous, smooth scraping paths that accommodate geometric deviations without relying on complex fixtures. Unlike RGB-D correction-based methods that require pre-programmed initial trajectories, or CAD-driven offline programming that cannot adapt to manufacturing deviations, the proposed approach directly generates conformal scraping paths from measured geometry. Experimental results on a typical cabin section demonstrate that the generated trajectories accurately follow the surface normals, achieving a low standard deviation of 36 μm in adhesive layer thickness, indicating excellent thickness consistency and uniformity. Furthermore, the automated process reduced the total operation time to approximately 40 min, improving production efficiency by more than two times compared to manual operations, thereby validating the robustness and suitability of the method for high-precision batch manufacturing. Full article

Blueberry anthocyanins (BAs) exhibit strong antioxidant and lipid-regulating activities; however, their chemical instability and low oral bioavailability limit their practical application. In this study, two plant-based bilayer nanocarriers were developed using soybean lecithin as the lipid core and gum arabic (GA) or carrageenan (CGN) as the shell polysaccharide. The optimized systems achieved encapsulation efficiencies of 79.7% and 81.9%, respectively. Structural analyses showed that anthocyanins were stably incorporated into the carriers through multiple non-covalent interactions and transformed from a crystalline to an amorphous state. The two shell polysaccharides exhibited distinct conformation-dependent protective behaviors: GA provided better thermal protection, whereas CGN showed superior resistance to light, metal ions, ascorbic acid, and simulated intestinal digestion. After INFOGEST digestion, anthocyanin retention in the intestinal phase was 47% and 51% for the GA- and CGN-coated systems, respectively, and antioxidant activity was better preserved than in the free anthocyanin group. In an oleic-acid-induced HepG2 lipid accumulation model, the CGN carrier showed good biocompatibility and significantly enhanced the lipid-lowering effect of anthocyanins, with the most pronounced reduction in intracellular triglycerides. These results indicate that the CGN carrier has considerable potential for maintaining anthocyanin stability, modulating digestive behavior, and enhancing biological efficacy, and provide a reference for the design of plant-based delivery systems for bioactive ingredients. Full article

The inherent nonlinearity and uncertainty of wind power generation pose significant challenges to the security, stability, and economic operation of power grids. Therefore, accurate and reliable wind power forecasting is crucial for seamless grid integration and effective risk assessment. Existing forecasting models often focus on improving point-prediction accuracy while overlooking effective multivariate dependency modeling and reliable uncertainty quantification, limiting both the informativeness and reliability of their forecasts. This study proposes a Fractional-order Momentum optimized Evidential iTransformer (FoM-EiT) for short-term wind power forecasting from multivariate time series. The proposed model integrates cyclic feature encoding for periodic variables, an inverted Transformer for variable-wise interaction learning, and an evidential output head that jointly produces point forecasts and uncertainty estimates from a shared representation. The proposed fractional-order momentum (FoM) optimization accumulates gradient history over an extended window, thereby smoothing oscillations caused by gradient competition and stabilizing the joint training process. Experiments on four real-world wind farms from different geographical regions show that FoM-EiT achieves competitive point forecasting performance, with $R^2$ values of 0.6342, 0.8211, 0.7844, and 0.9161, and the Wilcoxon signed-rank test indicates that its advantages over the baselines are statistically significant in the vast majority of comparisons. For uncertainty quantification, FoM-EiT achieves Prediction Interval Coverage Probability (PICP) values of 0.9492, 0.9682, 0.9709, and 0.9498, while the Winkler score results further show that its prediction intervals outperform the conformal prediction and quantile regression baselines in terms of overall interval quality. These results indicate that FoM-EiT provides both accurate forecasts and trustworthy uncertainty information, making it a practical tool for dispatch, reserve allocation, and risk-aware short-term power system operation. Full article

The prediction accuracy of RNA’s tertiary structure remains a core challenge in the field of computational biology. Existing models frequently encounter significant challenges due to the complexities of diverse topologies and the intricate nature of long-range interactions. We introduce RNAFoldDiff, a generative framework that integrates a sequence-aware graph transformer with a geometric diffusion process for end-to-end RNA 3D structure prediction. RNA sequences and secondary structures are converted into graph representations that capture backbone connectivity and base pair topology. The transformer models local motifs and global dependencies, while the diffusion module iteratively denoises coordinates into physically consistent conformations. The model was pretrained on more than 15,000 structural motifs from the RNA 3D Hub and fine-tuned on complete RNAs from the RNA-Puzzles dataset. In benchmarking tests, RNAFold-Diff achieved an average root mean square deviation (RMSD) of 2.64 Å, a Global Distance Test (GDT) score of 68.7%, and a base pair accuracy of 89.5%, reducing RMSD by nearly 30% and improving GDT by 9 points compared to RoseTTAFoldNA. The framework also outperformed FARFAR2, SimRNA, and RNAformer. Ablation experiments confirmed the contributions of diffusion refinement, edge-aware graph encoding, and motif-level pretraining, while qualitative analyses showed biologically plausible folds including helices, junctions, and multiloops. By combining topology-aware graph learning with generative diffusion, RNAFoldDiff advances RNA tertiary structure modeling and provides a practical tool for RNA design, ribozyme analysis, and structure-guided drug discovery. Full article

Deep coalbed methane reservoirs are characterized by complex geological conditions, strong heterogeneity, and significant variations in in situ stress, posing challenges for accurate three-dimensional in situ stress prediction. To address the issues of strong dependence on rock mechanical parameters in traditional physical models, as well as the lack of physical constraints and poor generalization capability under small-sample conditions in purely data-driven methods, this paper proposes a LightGBM prediction model that integrates physical information and data clustering. A total of 1289 fracturing clusters in the DJ block are selected as the research objects. First, the K-means algorithm is used to divide the reservoir into three categories to reduce the impact of heterogeneity. Then, a LightGBM model is constructed for each category, and physical constraints based on Huang’s model and stress–gravity equilibrium are incorporated into the loss function to ensure that the prediction results conform to mechanical laws. Taking the fracturing clusters in Category I as an example, the proposed model achieves an MAPE of 2.78% and an R² of 0.89 on the test set. Comparative experiments show that the proposed model outperforms BP neural networks, random forests, and Transformers in prediction accuracy. Ablation experiments verify the independent contributions and synergistic effects of the clustering module and the physical information constraints. Transfer experiments demonstrate that the model has good applicability to blocks with similar geological conditions. This study provides an effective method for predicting in situ stress in deep coalbed methane reservoirs, balancing accuracy and physical interpretability. Full article

This study investigates peri-urban land management in Uttar Pradesh through a comparative analysis of Varanasi, Kanpur, and Prayagraj, focusing on the gap between planned frameworks and actual urban growth. As rapidly expanding Tier-II cities, they represent critical sites where formal planning intersects with complex peri-urban transformations. The study employs a mixed-method approach, combining GIS-based master plan conformance analysis using Effective Boundary Control (EBC) with semi-structured expert interviews. This integration enables both spatial measurement of urban expansion and interpretive understanding of underlying governance and institutional dynamics. The results reveal significant divergence between planned and observed development, particularly in peripheral areas, with clear variation across cities. Kanpur exhibits the highest level of non-conformance (EBC: 2.23), indicating weak boundary control and pronounced peri-urban sprawl. Varanasi also demonstrates substantial deviation (EBC: 2.06), reflecting persistent gaps between planning intent and implementation. In contrast, Prayagraj shows relatively stronger conformance (EBC: 1.04), though underlying challenges remain. These differences are shaped by local conditions, including land acquisition conflicts, fragmented governance structures, infrastructure deficits, and limited financial mechanisms. Importantly, the findings underscore that even where spatial conformity appears stronger, it does not necessarily translate into effective planning outcomes. The study concludes that peri-urban growth is not simply unplanned but is shaped by negotiated and context-specific processes. It highlights the need for adaptive, implementation-focused planning, stronger institutional capacity, and integrated financial strategies. By bridging spatial and qualitative analysis, the research provides a more comprehensive framework for understanding and managing peri-urban development in rapidly urbanizing regions. Full article

Gel-based flexible electronic sensors have emerged as a transformative class of materials for next-generation applications. These applications are wearable electronics, soft robotics, electronic skin (e-skin), and healthcare monitoring systems. Owing to their intrinsic softness, stretchability, and biocompatibility, gels provide an ideal platform for constructing highly deformable and skin-conformable sensing devices. This paper provides insight into emerging fabrication techniques, including 3D printing, bioprinting, and microfabrication. These techniques have facilitated the creation of complex architectures with improved sensitivity and scalability. The review also focuses on recent advancements that have focused on overcoming traditional limitations. These limitations are poor mechanical strength, dehydration, limited environmental stability, and low sensitivity. In particular, the incorporation of conductive fillers and ionic species has enabled a range of sensing mechanisms. These mechanisms include piezoresistive, capacitive, piezoelectric, and ionotronic responses. Therefore, it allows for the accurate detection of strain, pressure, temperature, and biochemical signals. Finally, this review provides a summary of future research, which is expected to focus on multifunctional integration, sustainable materials, and intelligent data processing. It provides pathways to the widespread adoption of gel-based flexible electronic sensors in both consumer and clinical applications. Full article

To improve the flavor quality and antioxidant activity of walnut gluten peptides, gluten was extracted from defatted walnut meal by alkaline solubilization and acid precipitation, hydrolyzed with alkaline protease to prepare antioxidant peptides, and further modified by the Maillard reaction. The optimal sugar source was selected by single-factor experiments, and reaction conditions were optimized by response surface methodology. Peptide conformational changes were characterized by UV, fluorescence, DSC, FTIR, and SEM, while changes in amino acid composition, flavor properties, and antioxidant activity were systematically evaluated. Fructose was identified as the optimal sugar source. The optimal reaction conditions were a peptide-to-sugar ratio of 1:1.2, 78.5 °C, initial pH 7.6, and 2 h reaction time, under which the sensory score reached 8.5 and DPPH radical scavenging activity reached 66.92%. Maillard modification markedly altered peptide conformation, as shown by increased UV absorbance, decreased intrinsic fluorescence intensity with a red shift, an increase in denaturation temperature from 80 °C to 100 °C, reduced α-helix content, increased β-sheet content, and transformation of the microstructure from a loose porous morphology to dense block-like aggregates. Free amino acid content increased initially and then decreased, whereas total essential amino acids were largely retained, indicating that the overall nutritional composition was preserved. However, further evaluation of digestibility and bioavailability is required to confirm nutritional value. These findings provide a feasible strategy for improving the flavor and functional properties of walnut gluten peptides and support their high-value utilization. Full article

Accurate prediction of protein function is fundamental to progress in biotechnology and biomedicine, yet progress remains severely hampered by the widening chasm between exponentially growing genomic data and the limited capacity for functional annotation. High-throughput sequencing and metagenomics have driven an explosion in sequence data that far outstrips experimental characterization. UniProt now contains over 203 million protein entries, of which only ~2% have been experimentally validated. This widening “sequence–function gap” exceeds the reach of traditional homology-based tools such as BLAST (v2.17.0) and HMMER (v3.2), which are inherently constrained by sequence identity thresholds. The emergence of Protein Language Models (PLMs), including ESM and ProtTrans, has introduced a transformative paradigm, thereby shifting functional inference from similarity-based retrieval to geometric reasoning within learned semantic spaces. Nevertheless, current approaches remain largely confined to unimodal or narrowly bimodal frameworks, failing to capture the inherently multidimensional determinants of enzymatic function, including active-site geometry, chemical reaction logic, and literature-embedded semantic context. This review systematically adopts a multimodal global-fusion perspective, elucidating how three-dimensional geometric features, chemical reaction semantics, and textual knowledge graphs are synergistically integrated around PLMs as a core backbone. We delineate complementary mechanisms and integration strategies that together enable fine-grained protein function annotation beyond the performance ceiling of single-sequence methods. Furthermore, we survey the translational potential of such frameworks from computational prediction to real biological applications, and critically examine persistent bottlenecks including activity cliffs, transition-state inference, and conformational dynamics. We identify the integration of physics-informed machine learning with dynamics-aware architectures as a pivotal direction toward a causal, mechanism-level understanding of protein function. Full article

The demand for flexible and wearable electronics has intensified the need for conformable, high-performance, and self-sustaining power sources. Flexible supercapacitors (FSCs) and flexible batteries (e.g., lithium-ion and lithium–sulfur) are promising owing to their high-power density, long cycle life, and mechanical flexibility. A transformative solution lies in integrating these storage devices with mechanical energy harvesters, particularly triboelectric nanogenerators (TENGs), to create autonomous self-charging power systems (SCPSs). TENGs exhibit high output, versatile operational modes, material flexibility, and efficient energy harvesting from body movements. This review provides an overview of the recent advances in flexible energy storage technologies, encompassing carbon-based materials, MXenes, polymers, metal oxides, metal–organic frameworks (MOFs), and their hybrid architectures. It discusses the synergistic integration of these storage devices with TENGs to realize multifunctional SCPSs. It also highlights the fundamental design principles of flexible devices, the critical interplay of materials and architecture, and the journey towards monolithic system integration. The review also underscores the importance of managing harvesters’ pulsed output for efficient storage. Finally, a critical analysis of the challenges, including the energy density–flexibility compromise, environmental stability, and safety, is presented, alongside a forward-looking perspective on commercialization pathways for these technologies to power the next generation of autonomous wearable and sustainable electronic systems. Full article

Pancreatic lipase (PL) inhibition is a promising dietary strategy for obesity management. In this study, the inhibitory mechanisms and structural basis of polyphenols extracted from different sugarcane fractions were investigated using in vitro enzyme assays, spectroscopy, and molecular docking analyses. PL inhibitory activity was evaluated using p-nitrophenyl laurate (pNPL) as the substrate, with all assays performed in triplicate and results statistically analyzed. Among the extracts, sugarcane peel polyphenols (SP) exhibited the strongest inhibition, with a half-maximal inhibitory concentration (IC₅₀) of 31.56 mg/mL, significantly lower than that of sugarcane juice polyphenols (SJ, 55.86 mg/mL) and sugarcane bagasse polyphenols (SB, 65.31 mg/mL). Enzyme kinetic analyses revealed a reversible mixed-type inhibition mechanism. In contrast to crude extracts, individual phenolic monomers showed substantially lower IC₅₀ values (0.13–1.33 mg/mL), highlighting the intrinsic dilution. Compositional analysis identified ferulic acid, gallic acid, chlorogenic acid, and schaftoside as key contributors to PL inhibition. Fourier transform infrared (FTIR) and fluorescence spectroscopy demonstrated that polyphenols altered PL secondary structure by modulating α-helix and β-sheet contents and perturbed the microenvironment of tryptophan (Trp) and tyrosine (Tyr) residues. Molecular docking further indicated that these compounds bind within or near the substrate-binding channel via hydrogen bonding and hydrophobic interactions, engaging critical residues including Ser152, His263, and Phe77, and potentially influencing conformational elements involved in active-site accessibility. Collectively, these results suggest that sugarcane, particularly its peel, represents a valuable natural source of PL inhibitors. Despite the relatively high IC₅₀ values of crude extracts, their inhibitory activity arises from multicomponent contributions and supports their potential application as dietary modulators of fat digestion rather than as pharmaceutical lipase inhibitors. Full article

Accurate fold models are of great significance for mineralization control, resource exploration, and underground engineering. However, existing automated modeling methods show difficulty in quantitatively describing fold development patterns and lack the available reference models required for multiple-point statistics and intelligent modeling techniques. This study proposes a novel three-dimensional (3D) fold-modeling method that integrates multiple-point-statistics-based pattern library construction with a long short-term memory (LSTM) network-based modeling framework. The multiple-point geostatistic is employed to quantify spatial distributions and correlations in geological data, thereby identifying the intrinsic structural patterns of folds. The extracted patterns are transformed into a training library that effectively represents the geological semantics and morphological diversity of folds, providing a reliable dataset for LSTM-based model training. An optimized ConvLSTM network is designed to ensure robust representation of fold complexity and variability. Based on the network, 3D models can be rapidly generated from geological profiles. Multiple experiments demonstrate that the proposed method can automatically produce 3D models that conform to realistic geological conditions and accurately reflect true fold geometries. The approach significantly improves modeling efficiency and geological feature representation, providing a reliable tool for geological engineering applications. Full article

Aflatoxin B1 (AFB1) is a potent mycotoxin threatening food and feed safety. Here, we report the identification and characterization of a Bacillus safensis-derived multicopper oxidase (BsaMCO) capable of efficient AFB1 detoxification. Recombinant BsaMCO exhibited robust in vitro activity, achieving >78% degradation of AFB1 under 24 h incubation at 37 °C. Optimization experiments revealed that enzyme concentration, pH, temperature, metal ions, and electron acceptors significantly influenced degradation efficiency, defining an operational window suitable for practical applications. LC–MS profiling suggested the presence of transformation products tentatively consistent with oxidative demethylation to aflatoxin P1 (AFP1) and with the formation of AFG2a-like products through subsequent hydration- and oxidation-related transformations. Molecular docking and 100 ns all-atom molecular dynamics (MD) simulations demonstrated stable binding of AFB1 in the T1 copper pocket. Van der Waals and electrostatic interactions, together with a persistent hydrogen bond at Gly323, facilitated single-electron transfer through the intramolecular T2/T3 copper cluster. Principal component and Gibbs free energy analyses confirmed a low-energy, stable conformational ensemble. HepG2 cell assays indicated that BsaMCO-degraded products substantially reduced cytotoxicity and apoptosis compared with native AFB1. Simulated feed experiments further validated enzymatic AFB1 degradation, with approximately 53% reduction after 24 h. Collectively, these findings establish BsaMCO as a safe and effective biocatalyst for AFB1 detoxification, providing mechanistic, structural, and cellular evidence supporting its application in food and feed safety. Full article

Despite the growing interest in orally disintegrating films (ODFs), developing soy protein isolate (SPI)-based ODFs with both rapid disintegration and high functional stability remains a challenge. This study developed a novel SPI-based ODF incorporated with a cinnamaldehyde/β-cyclodextrin (CA/β-CD) inclusion complex at varying concentrations (5–20%, w/w) to address this gap. The control ODF exhibited poor structural order, a slow disintegration rate, and weak antioxidant activity. The incorporation of an appropriate amount of CA/β-CD inclusion complex (10–15%) significantly improved the comprehensive properties of the ODFs. The inclusion complex facilitated the formation of an orderly, continuous network structure, leading to a substantial enhancement in tensile strength (TS), elongation at break (EAB), disintegration rate, thermal stability, and sustained antioxidant activity. An excessive inclusion complex concentration (20%) induced agglomeration, compromising the structural integrity and functionality of the ODF. FTIR and secondary structure analyses revealed that the enhanced hydrogen bonding between the CA/β-CD inclusion complex and the SPI matrix promoted the transformation of disordered protein structures into ordered conformations (β-sheets and α-helices). This structural ordering is the core mechanism driving the improved macroscopic physicochemical and functional properties of the ODFs. This study confirms that CA/β-CD inclusion complexes can enhance the performance of SPI-based ODFs and provide a highly promising delivery system for hydrophobic bioactive substances. Full article