In human–robot hybrid intelligent warehouses, pallets often come in various shapes and sizes, posing challenges for AGVs to automate pallet picking. This, in turn, reduces the overall operational efficiency of the warehouse. To address this issue, this paper proposes a lightweight component segmentation network using a dual-attention mechanism to achieve precise segmentation of the pallet’s stringer board and accurate localization of the pallet slots. To overcome the challenge of redundant computations in existing semantic segmentation models, which are unable to balance spatial details and high-level semantic information, this network utilizes a dual-branch attention mechanism within an encoder–decoder architecture to effectively capture spatial details. On this basis, a residual structure is introduced to reduce redundant network parameters, addressing issues like vanishing and exploding gradients during training. Due to the lack of a public pallet image segmentation dataset, the network was tested using a custom-made dataset. The results show that by extracting intermediate-, low-, and high-level features from dual-branch input images and integrating them to construct multi-scale images, precise segmentation of various types of pallets can be achieved with limited annotated images. Furthermore, to comprehensively evaluate the model’s robustness, additional pallet localization experiments were conducted under varying illumination conditions and background noise levels. The results demonstrate that the proposed method can effectively identify and locate multi-category pallet targets while maintaining high segmentation accuracy under different lighting conditions and background interferences, verifying the model’s robustness in complex warehousing environments. Compared to the traditional model, the proposed model in this paper achieves a 10.41% improvement in accuracy and a 32.8% increase in image processing speed. The segmentation network we proposed is used for pallet-positioning experiments and has achieved good positioning results in pallet images taken from different distances and angles.

Orthodontic treatment was accompanied by the remodeling of cancellous bone in alveolar bone (CBAB), which manifested as the increase or decrease in bone mineral density (BMD). BMD is closely related to the mechanical properties of the alveolar bone. Therefore, the aim of this study was to quantify the effect of BMD on its viscoelastic behavior and to assess orthodontic forces at different BMDs. A total of nine CBAB samples were cut from the cervical, middle, and apical regions of the right mandible between canine, premolars, and molars. After scanning with micro-computed tomography (micro-CT). The BMD of samples was measured and dynamic mechanical analysis (DMA) was performed. Based on the fourth-order generalized Maxwell model, a viscoelastic constitutive model characterizing the BMD variation was constructed. The BMD exhibited variations within different regions of the CBAB. The storage modulus is positively correlated with BMD, and the loss modulus is negatively correlated with BMD.

In laser-directed energy deposition (LDED) additive manufacturing, stress-induced deformation and cracking often occur unexpectedly, and, once initiated, they are difficult to remedy. To address this issue, we previously proposed the Dynamic Counter Method (DCM), which monitors internal stress based on deposition layer shrinkage, enabling real-time stress monitoring without damaging the component. To validate this method, we used AlSi10Mg material, which has a low melting point and high reflectivity, and developed a high-precision segmentation network based on DeeplabV3+ to test its ability to measure shrinkage in high-exposure images. Using a real-time reconstruction model, stress calculations were performed with DCM and thermal–mechanical coupling simulations, and the results were validated through XRD residual stress testing to confirm DCM’s accuracy in calculating internal stress in aluminum alloys. The results show that the DeeplabV3+ segmentation network accurately extracted deposition-layer contours and shrinkage information. Furthermore, DCM and thermal–mechanical coupling simulations showed good consistency in residual stress distribution, with all results falling within the experimental error range. In terms of stress evolution trends, DCM was also effective in predicting stress variations. Based on these findings, two loading strategies were proposed, and, for the first time, DCM’s application in online stress monitoring of large LDED components was validated, offering potential solutions for stress monitoring in large-scale assemblies.

Collagen fibers of the Periodontal ligament (PDL) play a crucial role in determining its mechanical properties. Based on this premise, we investigated the effect of the volume fraction of human PDL collagen fibers on the hyperelastic mechanical behavior under transient loading. Samples were obtained from different root regions (neck, middle, and apex) of the PDL, prepared from fresh human anterior teeth. The collagen fibers volume fraction in various regions of the PDL was quantified by staining techniques combined with image processing software. The collagen fiber volume fractions were found to be 60.3% in the neck region, 63.1% in the middle region, and 52.0% in the apex region. A new hyperelastic constitutive model was constructed based on the volume fraction. A uniaxial tensile test was conducted on these samples, and the accuracy of the constitutive model was validated by fitting the test data. Also, relevant model parameters were derived. The results demonstrated that human PDL exhibited hyperelastic mechanical properties on the condition of transient loading. With an increase in the volume fraction of collagen fibers, the tensile resistance of the PDL was enhanced, demonstrating more significant hyperelastic mechanical properties. The hyperelastic constitutive model showed a good fit with the experimental results (R² > 0.997), describing the hyperelastic mechanical properties of the human PDL effectively.

Tooth extraction and maximum anchorage assessment are key decision points in orthodontic treatment planning, yet existing machine learning models for orthodontic decision support often lack transparency, limiting their clinical interpretability and trustworthiness. In this study, we developed and externally validated an explainable orthodontic treatment decision-support model that integrates expert-derived Analytic Hierarchy Process (AHP) weighting with machine learning. A diagnostic indicator framework comprising 18 orthodontic variables was established through a literature review, clinical data analysis, and two rounds of expert surveys. A retrospective cohort of 485 patients receiving fixed-appliance orthodontic treatment was used for model development and internal validation. AHP-derived composite scores were incorporated into the machine learning models for two prediction tasks, namely tooth extraction and maximum anchorage requirement, and an expert-informed fuzzy-rule score was calculated from pretreatment indicators for the maximum anchorage task to capture clinically interpretable anchorage tendencies. Model performance was evaluated using ROC-AUC, F1 score, precision, recall, PR-AUC, calibration analysis, and decision curve analysis, while SHAP was applied to interpret feature contributions. The AHP-RF extraction model and AHP-enhanced LR maximum anchorage model achieved the highest AUCs among the compared models (0.864 and 0.822, respectively), although paired DeLong tests showed no significant differences from the closest competing models. SHAP analysis identified lower lip-to-E-line distance, U1-NA, and the AHP composite score as important predictors, indicating consistency between model outputs and clinical reasoning. In the external validation cohort, the extraction model correctly classified 57 of 74 cases, and the maximum anchorage model correctly classified 24 of 29 cases, supporting the preliminary transportability of the proposed framework. These results suggest that integrating AHP-derived expert knowledge with machine learning provides an explainable and clinically interpretable decision-support model for orthodontic treatment planning, with potential value in improving standardized, evidence-informed, and patient-specific orthodontic decision-making.