Search Results (603)

Low-Dose Computed Tomography (LDCT) reduces radiation exposure but introduces severe quantum noise and streak artifacts that degrade image quality. To address these challenges, we propose the Physics-Guided Sequential State Space Transformer ($P{S}^{3}T$), a projection-domain denoising framework that combines sequential state-space modeling with a photon-aware attention mechanism to capture long-range dependencies across projection angles with linear computational complexity. A differentiable Filtered Backprojection (FBP) layer further enforces reconstruction-domain consistency during training. The proposed framework was evaluated on the Mayo Clinic LDCT and Projection Dataset using patient-level dataset partitioning. Experimental results demonstrate that $P{S}^{3}T$ consistently outperforms state-of-the-art methods, including DRL, SADiff, and GEDFormer, across the abdomen, head, and chest datasets. On the abdomen dataset, $P{S}^{3}T$ reached a peak PSNR of 42.40 dB, an SSIM of 0.9020, and the lowest RMSE of 0.0076 across anatomical regions. Statistical analysis using 95% confidence intervals and paired Wilcoxon signed-rank tests confirmed that these improvements were significant ($p<0.05$). Furthermore, $P{S}^{3}T$ achieved the lowest reconstruction consistency loss (0.0128 at epoch 50), demonstrating stable convergence and the effectiveness of incorporating acquisition physics into projection-domain LDCT denoising. Full article

This paper investigates a nonlinear chemotaxis model involving diffusion and chemically directed transport. A detailed Lie symmetry analysis is carried out for different parameter cases, and the corresponding determining equations are derived explicitly to classify the admitted Lie point symmetries. Using the obtained symmetry generators, several similarity reductions are constructed, including time-invariant, space-invariant, scaling, and traveling-wave reductions, with the reduced ordinary differential systems derived step by step. In particular, traveling-wave transformations reduce the governing PDE system to ordinary differential equations, from which several exact wave profiles are obtained, including multi-wave, breather-type, and kink-rational interaction solutions. The analytical structure and graphical behavior of these solutions are examined with the term soliton used only when the corresponding localization properties are satisfied. In addition, conservation-law approaches are employed to explore the structural properties of the model. Finally, the method of lines is used to obtain numerical approximations, allowing for a comparison with the analytical profiles and illustrating the influence of model parameters on the cell-density and chemical-concentration dynamics. Full article

With the advancement of Earth observation technology and the improvement of multisource data acquisition capabilities, heterogeneous remote sensing image change detection technology has become increasingly important. However, existing heterogeneous remote sensing image change detection methods rely largely on fixed network architectures, which makes adapting to complex modal differences and severe noise interference difficult. To address these issues, in this paper, a decoupled differential search-based graph change detection network (DDS-Net) is proposed. First, the model designs a decoupled differential search-based dual-stream graph encoder (DDSGE). By decoupling the search space from the optimization strategy, it automatically optimizes feature extraction operators and graph topologies for different modalities, thereby significantly increasing feature adaptability while reducing computational complexity. Second, to address nonlinear geometric distortions between heterogeneous images, in this paper, a heterogeneous spatiotemporal alignment module that is based on differential localization search (HSTAM) is proposed. This module uses a local soft attention mechanism to dynamically correct registration errors in the feature space. Furthermore, to suppress erroneous graph connections caused by noise, structural consistency and smooth denoising (SCSD) loss is introduced, and deep semantic feedback and graph smoothing regularization constraints are collaboratively used to dynamically generate graphs, thereby effectively increasing the internal consistency of the transformed graph and suppressing misconnection noise. Extensive experimental results demonstrate that this method significantly improves the robustness and accuracy of the model in complex registration error scenarios while maintaining computational efficiency. Full article

This study discusses the (2+1)-dimensional Kadomtsev–Petviashvili–Benjamin–Bona– Mahony equation, which emerges in weakly nonlinear dispersive plasma waves and shallow water dynamics in ocean engineering. Logarithmic dependent-variable transformations are applied to reconstruct a one-exponential tau function as a common one-soliton profile and derive its dispersion relation. This is a standard transformed solution listed as three normalized logarithmic maps, but not a new family of solutions. Only a one-exponential soliton is claimed, no two-soliton family and no arbitrary-N soliton family. The explicit rational rogue-wave families of the first, second, and third orders are derived using a modified version of a well-known center-shifted polynomial tau-function method that is applied to the KPBBM bilinear form, with both center parameters $\beta$ and $\gamma$ independent. The novelty is thus limited to the specific model and is not based on a new KPBBM equation or a fundamentally novel symbolic algorithm. The rogue-wave center translates in the longitudinal and transverse directions through $\beta$ and $\gamma$, respectively, for a fixed order N and fixed model parameters. They leave the pattern, localization width, background, and the arrangement of inner patterns unchanged. Lump solutions and lump–soliton interaction structures are also obtained and investigated. The auxiliary Hirota bilinear constraint and its reduced bilinear representation are explicitly given. The higher-degree equations found in the directional logarithmic maps are not new multilinear equations, but rather the denominator-cleared differential polynomial residuals. The validity of each solution family retained is guaranteed by means of analytical substitution or vanishing of symbolically identical-to-zero residual in the original KP–BBM equation. The two- and three-dimensional plots are used only to demonstrate the amplitude profile, localization, and propagation of the solutions, as verified by the analysis. In the weakly nonlinear, long-wave and weakly transverse regime where the KPBBM reduction is valid, these solutions give idealized mathematical representations of localization and interaction mechanisms. They are not predictive of coastal instability or offshore hydrodynamic loading, for which dimensional calibration and experimental/field validation would be necessary. Full article

Against the background of increasing styling convergence in the new energy vehicle (NEV) market, rear-end styling has gradually become a key visual interface for communicating brand identity, shaping product differentiation, and eliciting users’ Kansei cognition. However, existing Kansei design studies on automotive styling have mainly focused on whole-vehicle forms or front-face morphology, while systematic modeling methods for local rear-end styling remain limited. Under small-sample conditions, the nonlinear mapping between the Kansei semantic space and styling parameters also faces the risk of overfitting. To address these issues, this study proposes a data-driven Kansei Engineering (KE) framework integrating non-negative matrix factorization (NMF), grey relational analysis (GRA), and deep feedforward neural network (DFNN), aiming to achieve a continuous translation from Kansei need identification to parametric scheme generation for rear-end styling. First, the original seven-dimensional Kansei evaluations were aggregated into three latent Kansei dimensions through the non-negative low-rank decomposition of NMF. Second, GRA was used to screen key morphological features and reduce modeling complexity at the feature level. Third, DFNN and random forest (RF) were constructed as prediction models, and DFNN showed better average test RMSE and R² than RF. Finally, the optimal codes predicted by the DFNN were transformed into design schemes constrained by morphological coding, and their consistency in expressing the target Kansei images was verified, thereby establishing an engineering constraint-oriented and interpretable decoding pathway distinct from free-association-based Kansei design. The ablation experiment indicates that the performance advantage of the proposed framework does not arise solely from DFNN, but from the mathematical coupling among NMF-based semantic aggregation, GRA-based feature screening, and DFNN-based nonlinear mapping. This framework reformulates Kansei design as a hierarchical decomposition and modeling process, establishing a data-driven decision-support tool jointly driven by mathematical algorithms and artificial intelligence for NEV rear-end styling design. Full article

Background: Inflammatory bowel disease (IBD) is characterised by severe intestinal microbial dysbiosis. Most machine learning diagnostic models built on the longitudinal HMP2 cohort suffer serious data leakage from random sample-level cross-validation splitting, which leads to artificially inflated AUC values. Additionally, incomplete reporting of microbial preprocessing, random forest hyperparameters and multi-dimensional evaluation metrics reduces the reproducibility of existing research. Methods: We re-analysed the public HMP2 (IBDMDB) longitudinal metagenomic dataset containing 130 unique subjects (103 IBD/27 healthy controls) and 1627 longitudinal faecal samples. Raw 585 species were filtered by a minimum relative abundance of 1 × 10⁻⁵ and sample prevalence ≥20%, retaining 89 taxa; all 1135 metabolic pathways were retained. CLR transformation was applied to compositional abundance data. We performed Wilcoxon differential testing with Benjamini–Hochberg FDR correction, alpha/beta diversity analysis, and three random forest models (filtered species, all FDR-significant pathways, strictly filtered pathways). Critical improvements included subject-ID-stratified 5-fold cross-validation repeated 5 times, within-fold training-set-only feature importance calculation, and class weighting to balance unbalanced IBD/control samples. PERMANOVA with subject stratification and PERMDISP dispersion test were implemented with 999 fixed-seed permutations. Results: All four alpha diversity indices were significantly lower in IBD patients (all p < 0.0001). Subject-stratified PERMANOVA showed disease status only explained 1.18% of total Bray–Curtis community variance (R² = 0.0118, p = 1); PERMDISP detected significant group dispersion heterogeneity (p = 0.027). We identified 63 differentially abundant species and 695 perturbed pathways at FDR < 0.05. Canonical butyrate producers Faecalibacterium prausnitzii and Roseburia hominis showed no significant inter-group differences. Bootstrap 1000-resampling AUC 95% CIs indicated moderate classification performance: species model (0.626–0.705, mean AUC = 0.665), all-significant-pathway model (0.645–0.712, mean AUC = 0.679), strict-pathway model (0.620–0.685, mean AUC = 0.654). Alistipes putredinis and peptidoglycan biosynthesis I were the top taxonomic and pathway biomarkers, respectively. Conclusions: This study established a leakage-free machine learning pipeline for longitudinal microbiome cohorts via subject-level cross-validation splitting. The moderate AUC values eliminate false high performance caused by sample leakage, and we provide reliable candidate microbial and metabolic biomarkers for IBD. Restricted by single-cohort internal validation and unadjusted medication confounders, these markers still require independent multi-centre external verification before clinical translation. Full article

Backgorund/Objectives: Respiratory diseases represent a substantial global health burden and require effective localised pulmonary delivery strategies, particularly for poorly water-soluble therapeutic molecules. Nanoparticle-based drug delivery systems, especially those manufactured using microfluidics, have emerged as promising approaches to overcome pulmonary barriers, enhance local drug retention, and reduce systemic side effects. Among these nanocarriers, solid lipid nanoparticles (SLNs) and solid hybrid nanoparticles (SHNs) combine biocompatibility with controlled release and improved formulation stability. Methods: In this study, SLNs and lipid–chitosan SHNs were developed using microfluidic technology as candidate platforms for pulmonary drug delivery, with Cyclosporine A (CyA) used as a model hydrophobic cyclic peptide. Nanocarriers were produced using 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC) and cholesterol as lipids, with low-molecular-weight chitosan incorporated to obtain hybrid systems. Physicochemical properties were evaluated using dynamic light scattering (DLS) and ζ potential measurements, while morphology and structural organisation were investigated using transmission electron microscopy (TEM), Fourier-transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), and differential scanning calorimetry (DSC). Results: The microfluidic approach enabled the production of nanoparticles with controlled sizes below 200 nm, narrow size distributions, and good reproducibility. In addition, the SHNs exhibited a positive surface charge, high encapsulation efficiency (~80%), and good colloidal and thermal stability. In vitro release studies showed an initial burst release followed by sustained CyA release, reaching approximately 94% cumulative release within 6 h. The Korsmeyer–Peppas model was used as the standard kinetic model. No blank nanoparticles were used as controls in the EE and release assay. Conclusions: Overall, these findings support further investigation of microfluidic-produced lipid and hybrid nanoparticles as candidate platforms for pulmonary drug delivery. Full article

This paper presents a comparative study of two approaches for solving second-order ordinary differential equations arising from the membrane theory of shells of revolution. The considered equations originate from the rotational symmetry of shell structures, which enables the reduction of the governing partial differential equations to a sequence of ordinary differential equations corresponding to individual circumferential harmonics. The study compares the classical Differential Transform Method (DTM) with Physics-Informed Neural Networks (PINNs). Both initial value and boundary value problems are investigated, including benchmark examples with known analytical solutions and a systematic analysis of the influence of PINN architecture on the solution accuracy. For the PINN approach, the effects of the number of collocation points, hidden layers, and neurons per layer on the approximation error and training time are examined. The results demonstrate that DTM provides an efficient framework for constructing analytical solutions of initial value problems with minimal computational cost. However, its application to boundary value problems requires the introduction of additional auxiliary parameters and the solution of supplementary nonlinear equations, considerably increasing the analytical complexity of the procedure. In contrast, PINNs achieve high accuracy for both initial and boundary value problems while naturally incorporating boundary conditions through the loss function. The presented results demonstrate how the exploitation of geometric symmetry, combined with modern scientific machine learning techniques, provides an effective computational framework for solving differential equations arising in shell mechanics. Full article

In this paper, the new fractal modified Zakharov–Kuznetsov equation (fmZKe) defined on Cantor sets is investigated. The fmZKe is a non-differentiable model that arises naturally in mathematical physics, nonlinear wave theory, and plasma physics. The extended rational sine–cosine method is utilized to construct new optical soliton solutions of the model. The fmZKe is reduced to a non-differentiable ordinary differential equation by applying a non-differentiable wave transformation defined on Cantor sets. This reduction leads to a system of linear algebraic equations, which upon solving yields several exact solutions of the model. Furthermore, to establish a clear understanding of the model’s behavior, non-smooth graphical representations of the solutions are presented for various parameter values. The stability analysis of the newly obtained solutions in a classical sense is examined using stability theory, and the real-life applications of the results are highlighted. Full article

Delay differential equations form a distinct class of differential equations in which the derivative of the dependent variable depends on its value at an earlier time. This unique structure makes them particularly suitable for modeling phenomena in areas such as population dynamics, epidemiology, and the spread or control of diseases. In this study, a high-order Haar wavelet collocation method (HoHWCM) is proposed for the numerical solution of second-order delay differential equations (SoDDEs). The delay term is approximated using a Taylor expansion, transforming the SoDDE into a standard second-order differential equation (SoDE). The nonlinear terms are linearized through an innovative Taylor series-based approach, which also serves as an efficient iterative scheme. The resulting SoDE is then discretized using Haar wavelet basis functions, yielding a system of linear algebraic equations that is solved iteratively. This strategy eliminates the need for Newton’s or Broyden’s methods, thereby reducing computational cost and improving time efficiency. A variety of linear and nonlinear benchmark problems are solved to evaluate the accuracy and efficiency of the proposed method. Comparative results with established approaches from the literature demonstrate that the HoHWCM achieves higher accuracy, faster convergence, and reduced computational time, making it a highly effective alternative for solving such problems. Full article

Remote sensing image super-resolution (SR) aims to enhance spatial resolution and recover image details, which typically enhances the quality of optical remote sensing imagery. However, interference from cloud haze cover and sensor noise often leads to distorted details and artifacts in reconstructed images of conventional deep learning SR approaches, significantly limiting reconstruction fidelity. To address these challenges, we propose a novel SR framework based on the flow matching paradigm and a diffusion transformer, named FlowT-SR, which achieves superior and reliable reconstruction quality by jointly mitigating sensor noise and thin cloud interference. First, an evolution path from low-resolution images to ground-truth images is constructed based on the optimal transport displacement interpolation mechanism, and the corresponding vector field that governs this evolution is employed as the supervision signal for subsequent model training. Then, a multi-scale interference suppression (MSIS) module is combined with a novel diffusion transformer network (DiTNet) to predict the vector field. The MSIS module performs preliminary denoising and captures the spatial distribution of thin cloud and haze in low-resolution images, providing degradation-aware feature representations for DiTNet. Subsequently, a DiTNet is presented to predict the evolution vector field obtained in the first stage, which consists of ten layers based on the diffusion transformer. By accurately predicting the vector field at any time step, the model effectively reduces the impact of cloud haze and noise interference to improve the reconstruction precision. Finally, driven by the predicted vector field along the evolution path, the SR remote sensing image is generated through solving the corresponding ordinary differential equation, yielding cloud-free and noise-reduced results. Extensive experiments on our dataset and the public CUHK Cloud Removal dataset demonstrate that FlowT-SR effectively suppresses cloud haze and noise interference, achieving superior reconstruction performance compared with current state-of-the-art methods in terms of both PSNR and SSIM. Full article

Background: Sustainable maritime logistics has become a strategic priority for achieving the European Green Deal objectives and accelerating the transition towards low-carbon and resource-efficient transport systems. Nevertheless, existing studies mainly focus on isolated sustainability dimensions, providing limited evidence on the heterogeneous transition patterns followed by European countries. This study proposes an integrated analytical framework to identify and characterise longitudinal transition patterns towards sustainable maritime logistics across Europe. Methods: A harmonised database was developed using Eurostat indicators covering European countries for the period 2015–2023. Following data preprocessing, missing-value treatment and standardisation, Principal Component Analysis (PCA) was employed to reduce dimensionality, while cluster analysis was applied to identify groups of countries sharing similar sustainability and logistics transition characteristics. Results: The proposed framework reveals that the transition towards sustainable maritime logistics does not follow a single homogeneous pattern but multiple differentiated pathways. The analysis identifies distinct country profiles associated with energy transition, modal integration, circular economy performance and environmental sustainability, highlighting heterogeneous convergence dynamics throughout the study period. Conclusions: The findings provide a robust and transferable benchmarking framework for monitoring sustainable maritime logistics transition at the European level. By identifying differentiated transition pathways rather than static rankings, the proposed approach offers valuable evidence for supporting strategic planning, policy design and decision-making aimed at accelerating the transformation of maritime logistics systems towards sustainability. Full article

Background: Prolonged exposure to microgravity during spaceflight leads to significant bone loss. The phenomenon of weightlessness-induced bone loss (WIBL) continues to intensify as the flight time increases and this has attracted considerable attention of scholars. Methods: In this study, proteomic analysis was performed to identify potential signaling pathways involved in WIBL. Subsequently, the effectiveness of Atractylodes macrocephala polysaccharide (AMP) 1-1 on WIBL was investigated using a rat hindlimb suspension (HLS) model and a co-culture model of osteoblasts and RAW264.7 cell line. Results: It was found that AMP1-1 treatment improved bone microarchitecture, reduced the bone transformation rate, and enhanced bone biomechanical properties in the HLS model. In the cell co-culture system, AMP1-1 significantly inhibited osteoclastic differentiation, promoted osteoblastic differentiation, and attenuated osteoblastic apoptosis. The interaction between the nuclear factor erythroid 2-related factor 2 (Nrf2) system and antioxidant enzymes represented a key pathway mediating WIBL. Further investigation confirmed that AMP1-1 facilitated the nuclear translocation of Nrf2, up-regulated heme oxygenase-1 protein expression, and enhanced the transcription of downstream antioxidant enzymes. Consequently, it maintained bone homeostasis and protected against bone loss. Conclusions: Our findings demonstrate that AMP1-1 protects against WIBL by modulating the Nrf2 pathway and suppressing oxidative stress, offering a potential therapeutic strategy for mitigating bone loss in weightless environments. Full article

We study a nonlocal boundary value problem for a system of functional-differential equations with involution and an additional parameter. The proposed approach is based on a parameterization method developed by D. Dzhumabaev. The original boundary value problem is transformed into an equivalent Cauchy problem posed at the midpoint of the interval, together with a system of algebraic equations for the unknown parameters. By exploiting the symmetry and antisymmetry properties of the solution, we reduce the resulting Cauchy problem to a system of coupled Volterra integral equations of the second kind. This reduction makes it possible to derive explicit solution representations and to establish necessary and sufficient conditions for unique solvability in terms of the invertibility of the matrix generated by the boundary conditions. Finally, a first-order functional-differential equation is analyzed to demonstrate the applicability of the proposed method and to illustrate the obtained solvability conditions. Full article

This paper examines the (2 + 1)-dimensional complex modified Korteweg–de Vries equation, which describes the intricate motion of water particles from the surface to the bottom. By applying an appropriate wave transformation that reduces the governing nonlinear partial differential equations to an ordinary differential system, we conduct a comprehensive bifurcation analysis that identifies equilibrium points and characterizes their phase-space properties. Under periodic perturbations, the system exhibits bifurcations, quasi-periodicity, multistability, and chaotic behavior, supported by Lyapunov exponents, time-series evolution, phase portraits, and Poincaré sections. To obtain exact soliton solutions of various types, we employ both the dynamical system method and the generalized double auxiliary equation method. The physical features of the solutions are depicted using 2D and 3D representations of their real, imaginary, and absolute components. A comparison with related studies shows that the proposed approaches not only reproduce previously reported solutions but also yield broader and more general soliton families. Full article