Purpose To compare the clinical outcomes and biomechanical performance of digitally fabricated polyether-ether-ketone (PEEK) complete dentures with conventional polymethyl methacrylate (PMMA) dentures using finite element analysis (FEA). Methods Ten edentulous patients were enrolled. Each received both conventional PMMA dentures and digitally duplicated PEEK dentures. Tissue adaptation was evaluated using light-body silicone and 3D deviation analysis. Masticatory efficiency was assessed spectrophotometrically, and patient satisfaction via questionnaires. Paired t-tests were used for comparisons. The coefficient of friction between PEEK and mucosa was measured in vitro for the first time. Together with the concurrently measured PMMA–mucosa friction coefficient, these values were incorporated into finite element analysis software to evaluate the stress distribution and displacement of the dentures, mucosa and alveolar bone under centric and lateral occlusal conditions. Results No significant differences were found between groups in denture–tissue adaptation or patient satisfaction at 1, 3 and 6 months (P > .05). However, PEEK dentures showed significantly higher masticatory efficiency (P < .01). FEA demonstrated more uniform mucosal stress distribution and reduced denture deformation in the PEEK group under both occlusal conditions. Alveolar bone stress distribution was similar and remained within physiological limits in both groups. Conclusions Digitally fabricated PEEK complete dentures achieve comparable adaptation and satisfaction to PMMA dentures, while providing improved masticatory efficiency and favourable biomechanical performance. Clinical Significance PEEK complete dentures fabricated via digital workflows show strong potential as a reliable alternative to conventional dentures, with promising clinical applicability.
Urban road networks are vulnerable to extreme disasters, where secondary disasters frequently exacerbate primary impacts. To enhance lifecycle resilience, this study proposes a distributionally robust optimization (DRO) framework integrating pre-disaster reinforcement with post-disaster restoration scheduling. We formulate a two-stage DRO model: the first stage determines reinforcement strategies to mitigate capacity degradation, while the second stage schedules limited repair resources. To characterize spatially correlated impacts, we propose a concentric scenario generation scheme that represents the progressive expansion of network damage. In addition, we construct a decision-dependent ambiguity set to characterize distributional uncertainty under incomplete disaster information. To improve computational tractability, we develop a constraint generation algorithm, which iteratively identifies binding constraints. Extensive computational experiments are conducted on randomized networks, the Sioux Falls benchmark, and the Beijing Miyun road network. Results highlight the superior out-of-sample performance of the proposed DRO model and quantify the value of distributional robustness in resilience-oriented transportation planning.
At present, low-dimensional chaotic systems have deficiencies such as insufficient randomness, limited key space, and uneven sequence distribution, which make the security performance and anti-attack ability of the color image encryption algorithms constructed based on them insufficient and easy to be cracked, thus restricting the application of chaotic theory in the field of image encryption. In response to the above issues, this paper first constructs a three-dimensional dynamical system and proposes a three-dimensional logarithmic-exponential type trigonometrically (sin, cos) coupled chaotic dynamical system (3D-LECCS), this 3D-LECCS is explicitly defined as a globally bounded volume-expanding hyperchaotic system. Meanwhile, through bifurcation diagrams, Lyapunov exponents, sample entropy, permutation entropy, 0–1 chaotic state determination, NIST, and multistability analysis, the hyperchaotic characteristics and complex phase space motion behavior of 3D-LECCS are verified. Secondly, by leveraging the cryptographic properties of this system, a dynamic mapping cascaded encoding encryption mechanism for color images driven by 3D-LECCS (LECCS-CIEA) is designed. This scheme builds a strong nonlinear coupling correlation among the RGB three channels of color images through dynamic spatial partitioning cross-channel remapping and encoding cascaded XOR diffusion strategy. Finally, through theoretical analysis and simulation experiments, it is verified that LECCS-CIEA has good security and anti-attack ability. At the same time, the excellent chaotic performance of 3D-LECCS is also verified, confirming its reliability as an encryption entropy source.
High-volume data transmission in Internet of Things (IoT) networks demands the establishment of high-quality multi-hop communication paths at the network layer. To address this challenge, this paper proposes a joint routing and resource allocation framework that simultaneously optimizes relay node selection and transmit power allocation under decentralized manner. The proposed approach leverages deep reinforcement learning (DRL) to enable hop-by-hop decision-making with only local observations. To mitigate the partial observability stemmed from limited perception range of node, a transformer-based architecture is integrated into the DRL agent to enable the fusion of observations and actions collected along the established path. Specifically, the encoder module aggregates historical and frontier observations along the forwarding path, while the decoder module models temporal dependencies among historical actions. The fused representation is utilized to generate optimal decisions for next-hop node selection and power allocation. After each action execution, a one-hot encoded placeholder of the chosen action is fed back into the decoder module for subsequent decisions. Finally, numerical simulations demonstrate that the proposed transformer-enhanced DRL framework significantly outperforms state-of-the-art baselines in terms of end-to-end path quality, and robustness under decentralized IoT network configuration.
The rapid assessment of constructed facilities after extreme events is a knowledge-intensive task critical for effective emergency management. However, methodologies for automated, object-level damage assessment at scale remain underdeveloped, often lacking fine-grained interpretability or scalability. This paper introduces a framework that integrates instance segmentation with temporal Vision Language Model (VLM), which is empowered with visual damage reasoning capabilities through fine-tuning on domain-specific knowledge, for the automated and interpretable assessment of structural assets from satellite imagery. Our three-stage approach synergizes: high-precision segmentation via a modified Segment Anything Model (SAM); spatiotemporal data pairing to isolate asset-specific changes; and BDAChat, the first temporal VLM fine-tuned for object-level damage assessment. Unlike traditional black-box models, BDAChat provides both high-accuracy damage classification and causal interpretations, serving as an intelligent damage inference system. The framework’s effectiveness and scalability are validated through the Lahaina wildfire and hurricane Ian case study. This modular framework automates and accelerates the object-level building damage assessment process, demonstrating significant potential for real-time building damage evaluation and resilient infrastructure planning. The code and dataset are available at https://github.com/WangYong921/BDAChat.
