Search Results (4,252)

Patterned fabrics are characterized by strong periodic and symmetric structures, and defect detection in such materials is essentially the task of identifying local disruptions of global texture symmetry. Conventional low-rank decomposition methods separate defect-free regions as low-rank and defects as sparse components, yet singular value decomposition (SVD)-based formulations inevitably lose structural details, hindering faithful recovery of symmetric background patterns. Autoencoder (AE)-based reconstruction provides nonlinear modeling capacity but tends to over-reconstruct defective areas, thereby reducing the separability between anomalies and symmetric textures. To address these challenges, this study proposes WOT-AE (Weighted Optimal Transport Autoencoder), a unified framework that exploits the inherent symmetry of patterned fabrics for robust defect detection. The framework integrates three key components: (1) AE-based low-rank modeling, which replaces SVD to preserve fine-grained repetitive patterns; (2) weighted sparse isolation guided by pixel-level priors, which suppresses false positives in symmetric but defect-free regions; and (3) optimal transport alignment in the encoder feature space, which enforces distributional consistency of symmetric textures while allowing deviations caused by asymmetric defects. Through extensive experiments on benchmark patterned fabric datasets, WOT-AE demonstrates superior performance over six state-of-the-art methods, achieving more accurate detection of symmetry-breaking defects with improved robustness. Full article

Cell population and vascular vessel distribution analysis in membrane-based scaffolds for tissue engineering is crucial. Biomimetic nanostructured membranes of methyl methacrylate/hydroxyethyl methacrylate and methyl acrylate/hydroxyethyl acrylate (MMA)1-co-(HEMA)1/(MA)3-co-(HEA)2 loaded with 5% wt SiO2-nanoparticles (Si-M) were doped with zinc (Zn-M) or doxycycline (Dox-M). Critical bone defects were effectuated on six New Zealand-bred rabbit skulls and then they were covered with the membrane-based scaffolds. After six weeks, bone cell population in terms of osteoblasts, osteoclasts, osteocytes, fibroblasts, and M1 and M2 macrophages and vasculature was determined. The areas of interest were the space above (over) and below (under) the membrane, apart from the interior (inner) compartment. All membranes showed that vasculature and most cell types were more abundant under the membrane than in the inner or above regions. Quantitatively, osteoblast density increased by approximately 35% in Zn-M and 25% in Si-M compared with Dox-M. Osteoclast counts decreased by about 78% in Dox-M, indicating strong inhibition of bone resorption. Vascular structures were nearly twofold more frequent under the membranes, particularly in Si-M, while fibroblast presence remained moderate and evenly distributed. The M1/M2 macrophage ratio was higher in Zn-M, reflecting a transient pro-inflammatory state, whereas Dox-M favored an anti-inflammatory, pro-regenerative profile. These results indicate that the biomimetic electrospun membranes functioned as architectural templates that provided favorable microenvironments for cell colonization, angiogenesis, and early bone regeneration in a preclinical in vivo model. Zn-M membranes appear suitable for early osteogenic stimulation, while Dox-M membranes may be advantageous in clinical contexts requiring modulation of inflammation and osteoclastic activity. Full article

Aerial robots are a well-established solution for exploration, monitoring, and inspection, thanks to their superior maneuverability and agility. However, in many environments, they risk crashing and sustaining damage after collisions. Traditional methods focus on avoiding obstacles entirely, but these approaches can be limiting, particularly in cluttered spaces or on weight- and computationally constrained platforms such as drones. This paper presents a novel approach to enhance drone robustness and autonomy by developing a path recovery and adjustment method for a high-speed collision-resilient aerial robot equipped with lightweight, distributed tactile sensors. The proposed system explicitly models collisions using pre-collision velocities, rates and tactile feedback to predict post-collision dynamics, improving state estimation accuracy. Additionally, we introduce a computationally efficient vector-field-based path representation that guarantees convergence to a user-specified path, while naturally avoiding known obstacles. Post-collision, contact point locations are incorporated into the vector field as a repulsive potential, enabling the drone to avoid obstacles while naturally returning to its path. The effectiveness of this method is validated through Monte Carlo simulations and demonstrated on a physical prototype, showing successful path following, collision recovery, and adjustment at speeds up to $3.7$$\mathrm{m}$/$\mathrm{s}$. Full article

We propose MUSE++, an advanced and lightweight speech enhancement (SE) framework that builds upon the original MUSE architecture by introducing three key improvements: a Mamba-based state space model, dynamic SNR-driven data augmentation, and an augmented multi-objective loss function. First, we replace the original multi-path enhanced Taylor (MET) transformer block with the Mamba architecture, enabling substantial reductions in model complexity and parameter count while maintaining robust enhancement capability. Second, we adopt a dynamic training strategy that varies the signal-to-noise ratios (SNRs) across diverse speech samples, promoting improved generalization to real-world acoustic scenarios. Third, we expand the model’s loss framework with additional objective measures, allowing the model to be empirically tuned towards both perceptual and objective SE metrics. Comprehensive experiments conducted on the VoiceBank-DEMAND dataset demonstrate that MUSE++ delivers consistently superior performance across standard evaluation metrics, including PESQ, CSIG, CBAK, COVL, SSNR, and STOI, while reducing the number of model parameters by over 65% compared to the baseline. These results highlight MUSE++ as a highly efficient and effective solution for speech enhancement, particularly in resource-constrained and real-time deployment scenarios. Full article

Low-light image enhancement in architectural scenes presents a considerable challenge for computer vision applications in construction engineering. Images captured in architectural settings during nighttime or under inadequate illumination often suffer from noise interference, low-light blurring, and obscured structural features. Although low-light image enhancement and deblurring are intrinsically linked when emphasizing architectural defects, conventional image restoration methods generally treat these tasks as separate entities. This paper introduces an efficient and robust Frequency-Space Recovery Network (FSRNet), specifically designed for low-light image enhancement in architectural contexts, tailored to the unique characteristics of such scenes. The encoder utilizes a Feature Refinement Feedforward Network (FRFN) to achieve precise enhancement of defect features while dynamically mitigating background redundancy. Coupled with a Frequency Response Module, it modifies the amplitude spectrum to amplify high-frequency components of defects and ensure balanced global illumination. The decoder utilizes InceptionDWConv2d modules to capture multi-directional and multi-scale features of cracks. When combined with a gating mechanism, it dynamically suppresses noise, restores the spatial continuity of defects, and eliminates blurring. This method also reduces computational costs in terms of parameters and MAC operations. To assess the effectiveness of the proposed approach in architectural contexts, this paper conducts a comprehensive study using low-light defect images from indoor concrete walls as a representative case. Experimental results indicate that FSRNet not only achieves state-of-the-art PSNR performance of 27.58 dB but also enhances the mAP of the downstream YOLOv8 detection model by 7.1%, while utilizing only 3.75 M parameters and 8.8 GMACs. These findings fully validate the superiority and practicality of the proposed method for low-light image enhancement tasks in architectural settings. Full article

Detection of changes in remote sensing relies on clean multi-temporal images, but cloud cover may considerably degrade image quality. Cloud removal, a critical image-restoration task, demands effective modeling of long-range spatial dependencies to reconstruct information under cloud occlusions. While Transformer-based models excel at handling such spatial modeling, their quadratic computational complexity limits practical application. The recently proposed Mamba, a state space model, offers a computationally efficient alternative for long-range modeling, but its inherent 1D sequential processing is ill-suited to capturing complex 2D spatial contexts in images. To bridge this gap, we propose the multi-pattern scanning Mamba (MPSM) block. Our MPSM block adapts the Mamba architecture for vision tasks by introducing a set of diverse scanning patterns that traverse features along horizontal, vertical, and diagonal paths. This multi-directional approach ensures that each feature aggregates comprehensive contextual information from the entire spatial domain. Furthermore, we introduce a dynamic path-aware (DPA) mechanism to adaptively recalibrate feature contributions from different scanning paths, enhancing the model’s focus on position-sensitive information. To effectively capture both global structures and local details, our MPSM blocks are embedded within a U-Net architecture enhanced with multi-scale supervision. Extensive experiments on the RICE1, RICE2, and T-CLOUD datasets demonstrate that our method achieves state-of-the-art performance while maintaining favorable computational efficiency. Full article

This study proposes a discrete multi-agent Q-learning framework for the online tuning of PID controllers in continuous dynamic systems with limited observability. The approach treats the adjustment of each PID gain ($k_p$, $k_i$, $k_d$) as an independent learning process, in which each agent operates within a discrete state space corresponding to its own gain and selects actions from a tripartite space (decrease, maintain, or increase its gain). The agents act simultaneously under fixed decision intervals, favoring their convergence by preserving quasi-stationary conditions of the perceived environment, while a shared cumulative global reward, composed of system parameters, time and control action penalties, and stability incentives, guides coordinated exploration toward control objectives. Implemented in Python, the framework was validated in two nonlinear control problems: a water-tank and inverted pendulum (cart-pole) systems. The agents achieved their initial convergence after approximately 300 and 500 episodes, respectively, with overall success rates of $49.6\%$ and $46.2\%$ in 5000 training episodes. The learning process exhibited sustained convergence toward effective PID configurations capable of stabilizing both systems without explicit dynamic models. These findings confirm the feasibility of the proposed low-complexity discrete reinforcement learning approach for online adaptive PID tuning, achieving interpretable and reproducible control policies and providing a new basis for future hybrid schemes that unite classical control theory and reinforcement learning agents. Full article

Background: Current fMRI (functional magnetic resonance imaging)-driven brain information decoding for visual image reconstruction techniques faces issues such as poor structural fidelity, inadequate model generalization, and unnatural visual image reconstruction in complex scenarios. Methods: To address these challenges, this study proposes a Dynamic Confidence Bayesian Adaptive Network (DCBAN). In this network model, deep nested Singular Value Decomposition is introduced to embed low-rank constraints into the deep learning model layers for fine-grained feature extraction, thus improving structural fidelity. The proposed Bayesian Adaptive Fractional Ridge Regression module, based on singular value space, dynamically adjusts the regularization parameters, significantly enhancing the decoder’s generalization ability under complex stimulus conditions. The constructed Dynamic Confidence Adaptive Diffusion Model module incorporates a confidence network and time decay strategy, dynamically adjusting the semantic injection strength during the generation phase, further enhancing the details and naturalness of the generated images. Results: The proposed DCBAN method is applied to the NSD, outperforming state-of-the-art methods by 8.41%, 0.6%, and 4.8% in PixCorr (0.361), Incep (96.0%), and CLIP (97.8%), respectively, achieving the current best performance in both structural and semantic fMRI visual image reconstruction. Conclusions: The DCBAN proposed in this thesis offers a novel solution for reconstructing visual images from fMRI signals, significantly enhancing the robustness and generative quality of the reconstructed images. Full article

We theoretically investigate the stability of double-Q square skyrmion crystals under uniaxial distortion. Using an effective spin model with frustrated exchange interactions and bond-dependent anisotropy in momentum space, we construct the low-temperature magnetic phase diagram via simulated annealing. Our results reveal that uniaxial distortion drives a phase transition from the skyrmion crystal to a single-Q conical spiral state when the ratio of exchange interactions parallel and perpendicular to the uniaxial axis is reduced to about 95%. We further find that topologically trivial double-Q states, which emerge in the low- and high-field regimes, are more robust against uniaxial distortion than the skyrmion crystal appearing in the intermediate-field regime. Finally, we examine the role of bond-dependent anisotropy and demonstrate that a finite relative magnitude of this anisotropy is crucial for stabilizing the skyrmion crystal, even under uniaxial distortion. These findings highlight the delicate interplay between lattice distortions and bond-dependent interactions in determining the stability of multiple-Q magnetic textures, and they provide useful guidance for experimental efforts to manipulate skyrmion crystal phases in centrosymmetric magnets. Full article

Multi-animal pose estimation is a critical technique for enabling fine-grained quantification of group animal behaviors, which holds significant scientific value for uncovering behavioral changes under space environmental factors such as microgravity and radiation. Currently, the China Space Station has conducted a series of space biology experiments involving typical model organisms, including Caenorhabditis elegans (C. elegans), zebrafish, and Drosophila. However, substantial differences in species types, body scales, and posture dynamics among these animals pose serious challenges to the generalization and robustness of traditional pose estimation methods. To address this, we propose a novel, flexible, and general single-stage multi-animal pose estimation method. The method constructs species-specific pose group representations based on anatomical priors, incorporates a multi-scale feature-sampling module to integrate shallow and deep visual cues, and employs a structure-guided learning mechanism to enhance keypoint localization robustness under occlusion and overlap. We evaluate our method on the SpaceAnimal dataset—the first public benchmark for pose estimation and tracking of model organisms in space—containing multi-species samples from both spaceflight and ground-based experiments. Our method achieves AP scores of 72.8%, 62.1%, and 67.1% on C. elegans, zebrafish, and Drosophila, respectively, surpassing the state-of-the-art performance. These findings demonstrate the effectiveness and robustness of the proposed method across species and imaging conditions, offering strong technical support for on-orbit behavior modeling and large-scale quantitative analysis. Full article

Bayesian optimization (BO) struggles with data scarcity and poor scalability in high-dimensional many-objective optimization problems. To address this, we propose Co-Evolutionary GAN–Bayesian Optimization (CoEGAN-BO), a novel framework that synergizes generative adversarial networks (GANs) with Bayesian co-evolutionary search for data-driven optimization. The GAN module generates synthetic samples conditioned on promising regions identified by BO, while a co-evolutionary mechanism maintains two interacting populations: one explores the GAN’s latent space for diversity, and the other exploits BO’s probabilistic model for convergence. A bi-stage infilling strategy further enhances efficiency: early iterations prioritize exploration via $L_p$-norm-based candidate selection, later switching to a max–min distance criterion for Pareto refinement. Experiments on expensive multi/many-objective benchmarks show that CoEGAN-BO outperforms four state-of-the-art surrogate-assisted algorithms, achieving superior convergence and diversity under limited evaluation budgets. Full article

Time-sensitive networking (TSN), a cutting-edge technology enabling efficient real-time communication and control, provides strong support for traditional Ethernet in terms of real-time performance, reliability, and deterministic transmission. In TSN systems, although time-triggered (TT) flows enjoy deterministic delay guarantees, audio video bridging (AVB) and best effort (BE) traffic still share link bandwidth through statistical multiplexing, a process that remains nondeterministic. This competition in shared memory switches adversely affects data transmission performance. In this paper, a priority queue threshold control policy is proposed and analyzed for mixed-critical traffic in time-sensitive networks. The core of this policy is to set independent queues for different types of traffic in the shared memory queuing system. To prevent low-priority traffic from monopolizing the shared buffer, its entry into the queue is blocked when buffer usage exceeds a preset threshold. A two-dimensional Markov chain is introduced to accurately construct the system’s queuing model. Through detailed analysis of the queuing model, the truncated chain method is used to decompose the two-dimensional state space into solvable one-dimensional sub-problems, and the approximate solution of the system’s steady-state distribution is derived. Based on this, the blocking probability, average queue length, and average queuing delay of different priority queues are accurately calculated. Finally, according to the optimization goal of the overall blocking probability of the system, the optimal threshold value is determined to achieve better system performance. Numerical results show that this strategy can effectively allocate the shared buffer space in multi-priority traffic scenarios. Compared with the conventional schemes, the queue blocking probability is reduced by approximately 40% to 60%. Full article

Rural resilience building has gained increasing scholarly attention, yet existing literature overlooks the temporal dynamics of resilience evolution and lacks an integrative framework to explain cross-level mechanisms. This paper uses a longitudinal case study to explore how rural resilience transitions from a low-equilibrium to a high-equilibrium state and how neo-endogenous practices emerge in a weak institutional context. The study reveals three key findings. First, the village’s resilience evolved through three phases—institutional intervention, community capital activation, and resilience self-reinforcement—driven by co-evolutionary interactions between an enabling government and the rural community. This process is marked by chain effects of multidimensional community capital (e.g., cultural capital enhancing social capital) and overflow effects from resilience amplification (e.g., multi-scalar network). Second, exogenous resources and endogenous community capital are critical in the neo-endogenous model, but their synergy relies on vertical institutional interventions that foster horizontal networks and enhance communities’ resource absorption capacity. Third, the government enables resilience building by creating a support ecosystem that transitions from institutionally bundled resources to a higher-order composite space, facilitated by urban–rural interactions and community restructuring. The study makes three theoretical contributions: (1) it proposes an analytical framework integrating an enabling government, community capital, and ecosystem upgrading, thus advancing beyond the current community capital-centric paradigm; (2) it introduces a three-phase process model that unpacks spatiotemporal interactions across urban-rural interfaces, multi-scalar networks, and state-community relations, addressing the limitations of static factor-based analyses; (3) it reconceptualizes the role of government as an “enabling government” that mediates local and extra-local resource interfaces, challenging the neo-endogenous theories’ neglect of institutional agency. These insights contribute to rural resilience scholarship through a complex adaptive systems lens and offer policy implications for synergistic urban-rural revitalization. Full article

In the hot strip rolling process, inter-stand speed coordination directly affects product quality and production stability. Traditional linear speed compensation strategies perform poorly under extreme conditions such as strip tension and strip piling, making it difficult to maintain stable loop control. This study proposes a speed compensation strategy that integrates Particle Swarm Optimization (PSO) with Model Predictive Control (MPC). Based on the mechanism of hot rolling, a nonlinear state-space model is constructed, in which the compensation parameter is treated as an optimization variable to formulate a rolling optimization problem. PSO is employed to globally solve the nonlinear MPC problem, yielding an optimal compensation sequence that adapts to disturbance variations. The proposed algorithm can adaptively adjust the speed compensation parameter under typical strip piling and strip tension conditions, thereby achieving stable loop regulation and maintaining the looper angle within the desired range. This effectively addresses the speed coordination problem under abnormal conditions in hot strip rolling, improving the control performance. The experimental results verify the effectiveness of the proposed method. Full article

Background/Objectives: Cancer is a chronic and heterogeneous disease, possessing molecular variation within a single type, resulting in its molecular subtypes. Cancer molecular subtyping offers biological insights into cancer variability, facilitating the development of personalized medicines. Various models have been proposed for cancer molecular subtyping, utilizing the high-dimensional transcriptomic, genomic, or proteomic data. The issue of data scarcity, characterized by high feature dimensionality and a limited sample size, remains a persistent problem.The objective of this research is to propose a deep learning framework, DeepCMS, that leverages the capabilities of feed-forward neural networks, gene set enrichment analysis, and feature selection to construct a well-representative subset of the feature space, thereby producing promising results. Methods: The gene expression data were transformed into enrichment scores, resulting in over 22,000 features. From those, the top 2000 features were selected, and deep learning was applied to these features. The encouraging outcomes indicate the efficacy of the proposed framework in terms of defining a well-representative feature space and accurately classifying cancer molecular subtypes. Results: DeepCMS consistently outperformed state-of-the-art models in aggregated accuracy, sensitivity, specificity, and balanced accuracy. The aggregated metrics surpassed 0.90 for all efficiency measures on independent test datasets, showing the generalizability and robustness of our framework. Although developed using colon cancer’s gene expression data, this approach may be applied to any gene expression data; a case study is also devised for illustration. Conclusions: Overall, the proposed DeepCMS framework enables the accurate and robust classification of cancer molecular subtypes using a compact and informative feature set, facilitating improved precision in oncology applications. Full article