Explore the vast range of titles available.

In an era of pervasive alienation, examining spatial alienation and its potential positive roles has become essential for understanding the evolution of human settlements and the built urban environment. This study focuses on the exterior spaces of large-scale public buildings in Shanghai and designs an empirical investigation based on the presumed correlation between spatial vitality and multi-alienation. The findings demonstrate that vibrant exterior spaces are inherently multi-alienated environments. Human behavior and perception show a high degree of consistency in this regard, suggesting that the core of spatial vitality lies in dynamic occupation grounded in publicness.

Background Programmed death ligand 1 (PD-L1) expression status, closely related to immunotherapy outcomes, is a reliable biomarker for screening patients who may benefit from immunotherapy. Here, we developed and validated an interpretable machine learning (ML) model based on contrast-enhanced computed tomography (CECT) radiomics for preoperatively predicting PD-L1 expression status in patients with gastric cancer (GC). Methods We retrospectively recruited 285 GC patients who underwent CECT and PD-L1 detection from two medical centers. A PD-L1 combined positive score (CPS) of ≥ 5 was considered to indicate a high PD-L1 expression status. Patients from center 1 were divided into training ( n  = 143) and validation sets ( n  = 62), and patients from center 2 were considered a test set ( n  = 80). Radiomics features were extracted from venous-phase CT images. After feature reduction and selection, 11 ML algorithms were employed to develop predictive models, and their performance in predicting PD-L1 expression status was evaluated using areas under receiver operating characteristic curves (AUCs). SHapley Additive exPlanations (SHAP) were used to interpret the optimal model and visualize the decision-making process for a single individual. Results Nine features significantly associated with PD-L1 expression status were ultimately selected to construct the predictive model. The light gradient-boosting machine (LGBM) model demonstrated the best performance for PD-L1 high expression status prediction in the training, validation, and test sets, with AUCs of 0.841(95% CI: 0.773, 0.908), 0.834 (95% CI:0.729, 0.939), and 0.822 (95% CI: 0.718, 0.926), respectively. The SHAP summary and bar plots illustrated that a feature’s value affected the feature’s impact attributed to the model. The SHAP waterfall plots were used to visualize the decision-making process for a single individual. Conclusion Our CT radiomics–based LGBM model may aid in preoperatively predicting PD-L1 expression status in GC patients, and the SHAP method may improve the interpretability of this model.

Voids are common defects in 3D woven composites because of the complicated manufacturing processes of the composites. In this study, a micro–meso multiscale analysis was conducted to evaluate the influence of voids on the mechanical properties of three-dimensional orthogonal woven composites. Statistical analysis was implemented to calculate the outputs of models under the different scales. A method is proposed to generate the reasonable mechanical properties of the microscale models considering randomly distributed voids and fiber filaments. The distributions of the generated properties agree well with the calculated results. These properties were utilized as inputs for the mesoscale models, in which void defects were also considered. The effects of these defects were calculated and investigated. The results indicate that tensile and shear strengths were more sensitive to the microscale voids, while the compressive strength was more influenced by mesoscale voids. The results of this study can provide a design basis for evaluating the quality of 3D woven composites with void defects.

High-strength glass fibre-reinforced composites (H-GFRPs) are widely used in various engineering fields because of their excellent mechanical properties and designability. The mechanical properties of H-GFRPs are more sensitive to temperature and humidity. Under high temperature and humidity conditions, the properties decrease greatly and the dispersion increases. Tensile, compressive, and in-plane shear tests were carried out on five batches of H-GFRPs under five different conditions, and the strength and stiffness properties under different test conditions were obtained. In this paper, the strength and stiffness properties of H-GFRPs under room temperature and hygrothermal conditions are statistically analysed based on macroscopic test data and the meso-bridging model. The results showed that under hygrothermal conditions, the dispersion of performance tended to decrease. The distribution types of other parameters are consistent with those under room temperature conditions, except for the transverse tensile modulus E22,t and longitudinal compressive strength Xc, which tend to follow a normal distribution. Among the four stiffness performance parameters, the correlation between v12 and the other three stiffness parameters was weak, whereas that between the other three stiffness parameters was strong.

Determination of the fracture angle and maximum exposure value of extended Puck’s 3D inter-fiber failure (IFF) criterion is of great importance for predicting the failure mechanism of unidirectional fiber-reinforced composites. In this paper, a reliable semi-analytical algorithm (RSAA) is presented for searching fracture angle and corresponding exposure value for the extended Puck’s failure criterion. One hundred million cases are tested for verifying the accuracy of the present and other algorithms on Python using the strength-value-stress-state combinations more universal than those in previous literatures. The reliability of previous algorithms is discussed and counterexamples are provided for illustration. The statistical results show RSAA is adequate for implementation in extended Puck’s criterion and much more reliable than previous algorithms. RSAA can correctly predict the results with a probability of over 99.999%.

A progressive damage model including the non-linear shear behavior was developed to investigate the failure mechanisms of composite laminates. The non-linear shear response of the T300/Cycom970 lamina was derived by cyclic loading-unloading tensile tests on [+45/-45]2S laminate coupons. The strain-based Hashin type failure criteria and ply discount method were employed to predict damage initiation and propagation. The model was implemented in commercial finite element software ABAQUS by a UMAT subroutine. The tensile behavior of open-hole tension specimen was predicted with this model. A reasonable good agreement was achieved between numerical predictions and experimental results.