A B S T R A C T The off-axis fatigue cracking behaviour of notched fibre metal laminates under constant amplitude loading conditions was investigated experimentally and numerically. It was found that the off-axis fatigue crack initiation life decreased as the off-axis angles increased. This indicated that the off-axis laminates raised the applied stress level in the aluminium (Al) layer and subsequently resulted in earlier cracking in the Al layer. The off-axis fatigue crack initiation lives of notched fibre metal laminates were predicted using lamination theory and an energy-based critical plane fatigue damage analysis from the literature. After a crack initiated in the Al layer, it was observed that the crack path angles of the off-axis specimens were neither perpendicular to the fibre nor to the loading direction. A finite-element model was established for predicting the crack path angles.A = angularity of a laminate b axial , b shear = fatigue strength exponents for axial and torsional loadings c axial , c shear = fatigue ductility exponents for axial and torsional loadings C i,j = compliance matrix of a laminate E, G = elastic and shear modulus K t,FML = stress concentration factor of a laminate K Al t,FML = stress concentration factor of Al layer in a laminate N f = fatigue life N i = fatigue initiation life r = directionality of a laminate S i,j = stiffness matrix of a laminate T = temperature difference between curing temperature and room temperature (RT) α x , α y = thermal expansion coefficients along x and y directions α lam = thermal expansion coefficient of a laminate α p = thermal expansion coefficient of layer p ε ϕ,p = strain for layer p ε x , ε y = strains along x and y directions ε 1, ε 2, ε 3 = principal strains ε f = axial fatigue ductility coefficient ε n = normal strain range θ, θ p = off-axis and crack path angles σ x , σ y =stress levels along x and y directions τ xy = shear stress in x, y coordinates

Thin composite films containing inclusions are commonly being developed as corrosion resistant, wear resistant coatings and so forth. For systems where the composite film is under residual stresses properly designed, a controlled debonding process of the inclusions can be used to reduce the stress levels in the film lowering the risk of through cracks in the film as well as the risk for the film's delamination from the substrate. In this paper, on the basis of the Eshelby equivalent inclusion theory, a general solution is derived by treating an inhomogeneous inclusion as a homogeneous one with transformation strain for the configuration force (CF) and stress intensity factor (SIF) between mode I crack and an inhomogeneous inclusion of arbitrary shape which undergoes some degree of stress-free transformation strain under plane stress loading conditions. Then the CF associated with the transformation is calculated from the work done during the transformation, from which some simplified approximate formulae are also presented for common inclusion shapes in order to provide a quick estimate for the effects of inclusion shape, location and size on the CF of plane stress model I crack. In comparison to conventional numerical approaches, the present solution provides a novel approach to explain the behavior of crack deflection/penetration and can be used for the optimization design of the composite films.

Background: The objective of this study was to develop a practical nomogram for predicting the cancer-specific survival (CSS) of patients with small-intestine adenocarcinoma . Methods: Patients diagnosed with small-intestine adenocarcinoma between 2010 and 2015 were selected for inclusion in this study from the Surveillance, Epidemiology, and End Results (SEER) database. The selected patients were randomly divided into the training and validation cohorts at a ratio of 7:3. The predictors of CSS were identified by applying both forward and backward stepwise selection methods in a Cox regression model . The performance of the nomogram was measured by the concordance index (C-index), the area under receiver operating characteristic curve (AUC) , calibration plots, the net reclassification improvement (NRI), the integrated discrimination improvement (IDI) , and decision-curve analysis (DCA). Results: Multivariate Cox regression indicated that factors including age at diagnosis, sex, marital status, insurance status, histology grade, SEER stage, surgery status, T stage, and N stage were independent covariates associated with CSS. These factors were used to construct a predictive model, which was built and virtualized by a nomogram. The C-index of the constructed nomogram was 0.850. The AUC values indicated that the established nomogram displayed better discrimination performance than did the seventh edition of the American Joint Committee on Cancer TNM staging system in predicting CSS. The IDI and NRI also showed that the nomogram exhibited superior performance in both the training and validation cohorts. Furthermore, the calibrated nomogram predicted survival rates that closely corresponded to actual survival rates, while the DCA demonstrated the considerable clinical usefulness of the nomogram. Conclusion: We have constructed a nomogram for predicting the CSS of small-intestine adenocarcinoma patients . This prognostic model may improve the ability of clinicians to predict survival in individual patients and provide them with treatment recommendations.

Background: The objective of this study was to develop a practical nomogram for predicting the cancer-specific survival (CSS) of patients with small-intestine adenocarcinoma. Methods: Patients diagnosed with small-intestine adenocarcinoma between 2010 and 2015 were selected for inclusion in this study from the Surveillance, Epidemiology, and End Results (SEER) database. The selected patients were randomly divided into the training and validation cohorts at a ratio of 7:3. The predictors of CSS were identified by applying both forward and backward stepwise selection methods in a Cox regression model. The performance of the nomogram was measured by the concordance index (C-index), the area under receiver operating characteristic curve (AUC), calibration plots, the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), and decision-curve analysis (DCA).Results: Multivariate Cox regression indicated that factors including age at diagnosis, sex, marital status, insurance status, histology grade, SEER stage, surgery status, T stage, and N stage were independent covariates associated with CSS. These factors were used to construct a predictive model, which was built and virtualized by a nomogram. The C-index of the constructed nomogram was 0.850. The AUC values indicated that the established nomogram displayed better discrimination performance than did the seventh edition of the American Joint Committee on Cancer TNM staging system in predicting CSS. The IDI and NRI also showed that the nomogram exhibited superior performance in both the training and validation cohorts. Furthermore, the calibrated nomogram predicted survival rates that closely corresponded to actual survival rates, while the DCA demonstrated the considerable clinical usefulness of the nomogram.Conclusion: We have constructed a nomogram for predicting the CSS of small-intestine adenocarcinoma patients. This prognostic model may improve the ability of clinicians to predict survival in individual patients and provide them with treatment recommendations.