Search Results (475)

Background: Human Metapneumovirus (HMPV) is a respiratory virus in the Pneumoviridae family. HMPV is an enveloped, negative-sense RNA virus encoding three surface proteins: SH, G, and F. The highly immunogenic fusion (F) protein is essential for viral entry and a key target for vaccine development. The F protein exists in two conformations: prefusion and postfusion. The prefusion form is highly immunogenic and considered a potent vaccine antigen. However, this conformation needs to be stabilized to improve its immunogenicity for effective vaccine development. Specific mutations are necessary to maintain the prefusion state and prevent it from changing to the postfusion form. Methods: In silico mutagenesis was performed on the C-terminal domain of the pre-F protein, focusing on five amino acids at positions 469 to 473 (LVDQS), using the established pre-F structure (PDB: 8W3Q) as the reference. The amino acid sequence was sequentially mutated based on hydrophobicity, resulting in mutants M1 (IIFLL), M2 (LLIVL), M3 (WWVLL), and M4 (YMWLL). Increasing hydrophobicity was found to enhance protein stability and structural rigidity. Results: Epitope mapping revealed that all mutants displayed significant B and T cell epitopes similar to the reference protein. The structure and stability of all mutants were analyzed using molecular dynamics simulations, free energy calculations, and secondary structure analysis. Based on the lowest RMSD, clash score, MolProbity value, stable radius of gyration, and low RMSF, the M1 mutant demonstrated superior structural stability. Conclusions: Our findings indicate that the M1 mutant of the pre-F protein could be the most stable and structurally accurate candidate for vaccine development against HMPV. Full article

This work establishes the first derivation of geometry-dependent Kirchhoff’s laws via conformal symmetry, enabling new types of self-sustaining circuits unattainable in classical lumped-element theory. Building on Bessel-Hagen’s extension of Noether’s theorem to Maxwell’s equations, we develop a conformal circuit formalism that fundamentally extends traditional circuit theory through two key innovations: (1) Geometry-dependent weighting factors ($w_i\propto a_i^{-1}$) in Kirchhoff’s laws derived from scaling symmetry; (2) A dilaton-like field ($\delta$) mediating energy exchange between circuits and conformal backgrounds. Unlike prior symmetry applications in electromagnetism, our approach directly maps the 15-parameter conformal group to component-level circuit transformations, predicting experimentally verifiable phenomena: (i) $10.2\%$ deviations from classical current division in RF splitters; (ii) $4.2\%$ resonant frequency shifts with $2.67\times$ Q-factor enhancement; (iii) Power-law scaling ($J_z\propto a^{-2}$) in cylindrical conductors. This theoretical framework proposes how conformal symmetry could enable novel circuit behaviors, including potential self-sustaining oscillations, subject to experimental validation. Full article

Conformal mappings between Riemannian spaces ${\overline{\mathbb{R}}}_N$ and ${\mathbb{R}}_N$ are defined by the explicit transformation of the metric tensor of the space ${\overline{\mathbb{R}}}_N$ to the metric tensor of the space ${\mathbb{R}}_N$. Geodesic mapping between these two Riemannian spaces is a transformation that transforms any geodesic line of the space ${\overline{\mathbb{R}}}_N$ to a geodesic line of the space ${\mathbb{R}}_N$. In this research, we defined an m-conformal line of a Riemannian space, which is geodesic if $m=0$. Based on this definition, we involved the concept of $(\overline{m},m)$-conformal mapping as a transformation ${\overline{\mathbb{R}}}_N\to {\mathbb{R}}_N$ in which any $\overline{m}$-conformal line of the space ${\overline{\mathbb{R}}}_N$ transforms to an m-conformal line of the space ${\mathbb{R}}_N$. The result of this research is the establishment of three invariants for these mappings. At the end of this research, we gave an example of a scalar geometrical object which may be used in physics. Full article

For addressing the technical challenges of photon denoising and water level extraction in ICESat-2 satellite-based water monitoring applications, this paper proposes an innovative solution integrating Gaussian function fitting with Hough transform. The method first employs histogram Gaussian fitting to achieve coarse denoising of water body regions. Subsequently, a probability attenuation model based on elevation differences between adjacent photons is constructed to accomplish refined denoising through iterative optimization of adaptive thresholds. Building upon this foundation, the Hough transform technique from image processing is introduced into photon cloud processing, enabling robust water level extraction from ICESat-2 data. Through rasterization, discrete photon distributions are converted into image space, where straight lines conforming to the photon distribution are then mapped as intersection points of sinusoidal curves in Hough space. Leveraging the noise-resistant characteristics of the Hough space accumulator, the interference from residual noise photons is effectively eliminated, thereby achieving high-precision water level line extraction. Experiments were conducted across five typical water bodies (Qinghai Lake, Long Land, Ganquan Island, Qilian Yu Islands, and Miyun Reservoir). The results demonstrate that the proposed denoising method outperforms DBSCAN and OPTICS algorithms in terms of accuracy, precision, recall, F1-score, and computational efficiency. In water level estimation, the absolute error of the Hough transform-based line detection method remains below 2 cm, significantly surpassing the performance of mean value, median value, and RANSAC algorithms. This study provides a novel technical framework for effective global water level monitoring. Full article

High-fidelity finite element analysis (FEA) plays a key role in structural engineering by enabling accurate simulation of displacement, stress, and internal forces under static loads. However, its high computational cost limits applicability in real-time control, iterative design, and large-scale uncertainty quantification. Surrogate modeling provides a computationally efficient alternative by learning input–output mappings from precomputed simulations. Yet, the performance of individual surrogates is often sensitive to data distribution and model assumptions. To enhance both accuracy and robustness, we propose a model averaging framework based on Jackknife Model Averaging (JMA) that integrates six surrogate models: polynomial response surfaces (PRSs), support vector regression (SVR), radial basis function (RBF) interpolation, eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), and Random Forest (RF). Three ensembles are formed: JMA1 (classical models), JMA2 (tree-based models), and JMA3 (all models). JMA assigns optimal convex weights using cross-validated out-of-fold errors without a meta-learner. We evaluate the framework on the Static Analysis Dataset with over 300,000 FEA simulations. Results show that JMA consistently outperforms individual models in root mean squared error, mean absolute error, and the coefficient of determination, while also producing tighter, better-calibrated conformal prediction intervals. These findings support JMA as an effective tool for surrogate-based structural analysis. Full article

We previously developed SA-conf, a method designed to quantify backbone structural variability in protein targets. This approach is based on the HMM-SA structural alphabet, which enables efficient and rapid comparison of local backbone conformations across multiple structures of a given target. In this study, SA-conf (version for python2.7) was applied to a dataset of 130 crystallographic chains of Bcl-xL, a protein involved in promoting cell survival. SA-conf quantified and mapped backbone structural variability, revealing the protein’s capacity for conformational rearrangement. Our results showed that while most mutations had minimal impact on backbone conformation, some were associated with long-range structural effects. By jointly analyzing residue flexibility and backbone rearrangements across apo and holo structures, SA-conf identified key regions where the backbone undergoes structural adjustments upon ligand binding. Notably, the $\alpha 2$–$\alpha 3$ region was shown to be a hotspot of structural plasticity, exhibiting ligand-specific conformational signatures. Furthermore, SA-conf enabled the construction of a structural map of the binding site, distinguishing a conserved anchoring core from flexible peripheral regions that contribute to ligand specificity. Overall, this study highlights SA-conf’s capacity to detect conformational changes in protein backbones upon ligand binding and to uncover structural determinants of selective ligand recognition. Full article

Solvent-exchange-induced in situ forming gel (ISG) refers to a drug delivery system that transforms from a solution state into a gel or solid matrix upon administration into the body and exposure to physiological aqueous fluid. This study investigates the molecular behavior and phase inversion process of cellulose acetate butyrate (CAB)-based in situ forming gel (ISG) formulations containing moxifloxacin (Mx) or benzydamine HCl (Bz) as model drugs dissolved in N-methyl pyrrolidone (NMP) using molecular dynamics (MD) simulations and density functional theory (DFT) calculations. The simulations reveal a solvent exchange mechanism, where the diffusion of water molecules replaces NMP, driving the formation of the CAB matrix. Bz exhibited faster diffusion and a more uniform distribution compared to Mx, which aggregated into clusters due to its larger molecular size. The analysis of the root mean square deviation (RMSD) and radius of gyration confirmed the faster diffusion of Bz, which adopted a more extended conformation, while Mx remained compact. The phase transformation was driven by the disruption of CAB-NMP hydrogen bonds, while CAB–water interactions remained limited, suggesting that CAB does not dissolve in water, facilitating matrix formation. The molecular configuration revealed that drug–CAB interactions were primarily governed by hydrophobic forces and van der Waals interactions rather than hydrogen bonding, controlling the release mechanism of both compounds. DFT calculations and electrostatic potential (ESP) maps illustrated that the acetyl group of CAB played a key role in drug–polymer interactions and that differences in CAB substitution degrees influenced the stability of drug-CAB complexes. Formation energy calculations indicated that Mx-CAB complexes were more stable than Bz-CAB complexes, resulting in a more prolonged release of Mx compared to Bz. Overall, this study provides valuable insights into the molecular behavior of CAB-based Mx-, Bz-ISG formulations. Full article

Accurate segmentation of cardiac MR images using deep neural networks is crucial for cardiac disease diagnosis and treatment planning, as it provides quantitative insights into heart anatomy and function. However, achieving high segmentation accuracy relies heavily on extensive, precisely annotated datasets, which are costly and time-consuming to obtain. This study addresses this challenge by proposing a novel data augmentation framework based on a condition-guided diffusion generative model, controlled by multiple cardiac labels. The framework aims to expand annotated cardiac MR datasets and significantly improve the performance of downstream cardiac segmentation tasks. The proposed generative data augmentation framework operates in two stages. First, a Label Diffusion Module is trained to unconditionally generate realistic multi-category spatial masks (encompassing regions such as the left ventricle, interventricular septum, and right ventricle) conforming to anatomical prior probabilities derived from noise. Second, cardiac MR images are generated conditioned on these semantic masks, ensuring a precise one-to-one mapping between synthetic labels and images through the integration of a spatially-adaptive normalization (SPADE) module for structural constraint during conditional model training. The effectiveness of this augmentation strategy is demonstrated using the U-Net model for segmentation on the enhanced 2D cardiac image dataset derived from the M&M Challenge. Results indicate that the proposed method effectively increases dataset sample numbers and significantly improves cardiac segmentation accuracy, achieving a 5% to 10% higher Dice Similarity Coefficient (DSC) compared to traditional data augmentation methods. Experiments further reveal a strong correlation between image generation quality and augmentation effectiveness. This framework offers a robust solution for data scarcity in cardiac image analysis, directly benefiting clinical applications. Full article

Nanoimprint lithography (NIL) has emerged as a promising sub-10 nm patterning at low cost; yet, robust process control remains difficult because of time-consuming physics-based simulators and labeled SEM data scarcity. We propose a data-efficient, two-stage deep-learning framework here that directly reconstructs post-imprint SEM images from binary design layouts and delivers calibrated pixel-by-pixel uncertainty simultaneously. First, a shallow U-Net is trained on conformalized quantile regression (CQR) to output 90% prediction intervals with statistically guaranteed coverage. Moreover, per-level errors on a small calibration dataset are designed to drive an outlier-weighted and encoder-frozen transfer fine-tuning phase that refines only the decoder, with its capacity explicitly focused on regions of spatial uncertainty. On independent test layouts, our proposed fine-tuned model significantly reduces the mean absolute error (MAE) from 0.0365 to 0.0255 and raises the coverage from 0.904 to 0.926, while cutting the labeled data and GPU time by 80% and 72%, respectively. The resultant uncertainty maps highlight spatial regions associated with error hotspots and support defect-aware optical proximity correction (OPC) with fewer guard-band iterations. Extending the current perspective beyond OPC, the innovatively model-agnostic and modular design of the pipeline here allows flexible integration into other critical stages of the semiconductor manufacturing workflow, such as imprinting, etching, and inspection. In these stages, such predictions are critical for achieving higher precision, efficiency, and overall process robustness in semiconductor manufacturing, which is the ultimate motivation of this study. Full article

Grasping objects with a high degree of anthropomorphism is a critical component in the field of highly anthropomorphic robotic grasping. However, the accuracy of contact maps and the irrationality of the grasping gesture become challenges for grasp generation. In this paper, we propose a reasonably improved generation scheme, called Diffangle-Grasp, consisting of two parts: contact map generation based on a conditional variational autoencoder (CVAE), sharing the potential space with the diffusion model, and optimized grasping generation, conforming to the physical laws and the natural pose. The experimental findings demonstrate that the proposed method effectively reduces the loss in contact map reconstruction by 9.59% in comparison with the base model. Additionally, it enhances the naturalness by 2.15%, elevates the success rate of grasping by 3.27%, reduces the penetration volume by 11.06%, and maintains the grasping simulation displacement. The comprehensive comparison and qualitative analysis with mainstream schemes also corroborate the rationality of the improvement. In this paper, we provide a comprehensive account of our contributions to enhancing the accuracy of contact maps and the naturalness of grasping gestures. We also offer a detailed technical feasibility analysis for robotic human grasping. Full article

Background/Objectives: Understanding the molecular interactions between small bioactive compounds and serum albumins is essential for drug development and pharmacokinetics. Noraucuparin, a biphenyl-type phytoalexin with promising pharmacological properties, has shown a strong binding affinity to bovine serum albumin (BSA), a model protein for drug transport. This study aims to elucidate the structural and energetic characteristics of the noraucuparin–BSA complex under physiological and slightly elevated temperatures. Methods: Microsecond-scale molecular dynamics (MD) simulations and Molecular Mechanics Generalized Born Surface Area (MMGBSA)-binding-free energy calculations were performed to investigate the interaction between noraucuparin and BSA at 298 K and 310 K. Conformational flexibility and per-residue energy decomposition analyses were conducted, along with interaction network mapping to assess ligand-induced rearrangements. Results: Noraucuparin preferentially binds to site II of BSA, near the ibuprofen-binding pocket, with stabilization driven by hydrogen bonding and hydrophobic interactions. Binding at 298 K notably increased the structural mobility of BSA, affecting its global conformational dynamics. Key residues, such as Trp213, Arg217, and Leu237, contributed significantly to complex stability, and the ligand induced localized rearrangements in the protein’s intramolecular interaction network. Conclusions: These findings offer insights into the dynamic behavior of the noraucuparin–BSA complex and enhance the understanding of serum albumin–ligand interactions, with potential implications for drug delivery systems. Full article

The analytical solutions for groundwater inflow into tunnels are usually developed under the condition of circular tunnels. However, real-world tunnels often have non-circular cross-sections, such as arched, lens-shaped, or egg-shaped profiles. Accurately assessing water inflow for these diverse tunnel shapes remains challenging. To address this gap, this study developed a closed-form analytical solution for water inflow into a deeply buried arched tunnel using the conformal mapping method. When the tunnel circumference degenerates to a circle, the analytical solution degenerates to the widely used Goodman’s equation. The solution also showed excellent agreement with numerical simulations carried out using COMSOL. Based on the analytical solution, the impact of various factors on water inflow Q was further discussed: (1) Q decreases as the boundary distance $D^{\ast }$ increases. And the boundary inclination angle ($\alpha -\pi /2$) significantly affects Q only when the boundary is close to the tunnel ($D^{\ast }<20$); (2) Q increases quickly with the upper arc radius $r_1^{\ast }$, while it shows minimal variation with the change in the lower arc radius $r_2^{\ast }$. The findings provide a theoretical foundation for characterizing water inflow into arched tunnels, thereby supporting improved tunnel planning and grouting system design. Full article

In recent years, deep learning-based end-to-end Text-To-Speech (TTS) models have made significant progress in enhancing speech naturalness and fluency. However, existing Variational Inference Text-to-Speech (VITS) models still face challenges such as insufficient pitch modeling, inadequate contextual dependency capture, and low inference efficiency in the decoder. To address these issues, this paper proposes an improved TTS framework named Q-VITS. Q-VITS incorporates Rotary Position Embedding (RoPE) into the text encoder to enhance long-sequence modeling, adopts a frame-level prior modeling strategy to optimize one-to-many mappings, and designs a style extractor based on a diffusion model for controllable style rendering. Additionally, the proposed decoder ConfoGAN integrates explicit F0 modeling, Pseudo-Quadrature Mirror Filter (PQMF) multi-band synthesis and Conformer structure. The experimental results demonstrate that Q-VITS outperforms the VITS in terms of speech quality, pitch accuracy, and inference efficiency in both subjective Mean Opinion Score (MOS) and objective Mel-Cepstral Distortion (MCD) and Root Mean Square Error (RMSE) evaluations on a single-speaker dataset, achieving performance close to ground-truth audio. These improvements provide an effective solution for efficient and controllable speech synthesis. Full article

Dynamic impact experiments based on high-speed photography and digital image correlation (DIC) techniques were carried out on sandstone specimens containing arched holes to investigate the effect of the incident angle. In addition, the complex function method based on conformal mapping was used to theoretically calculate the transient dynamic stress distributions around the arched holes. The test results indicated that the strength and modulus of elasticity of the specimens under dynamic impact decreased and then increased with the increase of the inclination angle of the holes from 0 to 90° at intervals of 15°, reaching a minimum value at 60°, due to the large stress concentration at this angle leading to the shear failure of the specimen. During the experiment, rock debris ejections, spalling, and heaving were observed around the holes, and the rock debris ejections served as an indicator to identify the early fracture. The damage mechanism around the holes was revealed theoretically, i.e., the considerable compressive stress concentration in the perpendicular incidence direction around the arched hole and the tensile stress concentration on the incidence side led to the initiation of the damage around the cavity, and the theoretical results were in satisfactory agreement with the experimental results. In addition, the effect of the initial stress on the dynamic response of the arched tunnel was discussed. Full article

With the rapid development of wearable electronics, traditional rigid thermal management materials face limitations in flexibility, conformability, and multi-physics adaptability. Low-dimensional carbon materials such as graphene and carbon nanotubes combine ultrahigh thermal conductivity with outstanding mechanical compliance, making them promising building blocks for flexible thermal regulation. This review summarizes recent advances in integrating these materials into textile architectures, mapping the evolution of this emerging field. Key topics include phonon-dominated heat transfer mechanisms, strategies for modulating interfacial thermal resistance, and dimensional effects across scales; beyond these intrinsic factors, hierarchical textile configurations further tailor macroscopic performance. We highlight how one-dimensional fiber bundles, two-dimensional woven fabrics, and three-dimensional porous networks construct multi-directional thermal pathways while enhancing porosity and stress tolerance. As for practical applications, the performance of carbon-based textiles in wearable systems, flexible electronic packaging, and thermal coatings is also critically assessed. Current obstacles—namely limited manufacturing scalability, interfacial mismatches, and thermal performance degradation under repeated deformation—are analyzed. To overcome these challenges, future studies should prioritize the co-design of structural and thermo-mechanical properties, the integration of multiple functionalities, and optimization guided by data-driven approaches. This review thus lays a solid foundation for advancing carbon-based smart textiles toward next-generation flexible thermal management technologies. Full article