Search

We call the solution of a kind of weighted Laplace equation complex-valued kernel $$\alpha $$ -harmonic mappings. In this paper, some geometric properties of the complex-valued kernel $$\alpha $$ -harmonic mappings, such as area, fully starlikeness and fully convexity of order $$\gamma $$ , $$\gamma \in [0,1)$$ , are explored. Furthermore, for a class of given boundary function, the Rado–Kneser–Choquet-type theorem is obtained.

A novel machine learning aided structural reliability analysis for functionally graded frame structures against static loading is proposed. The uncertain system parameters, which include the material properties, dimensions of structural members, applied loads, as well as the degree of gradation of the functionally graded material (FGM), can be incorporated within a unified structural reliability analysis framework. A 3D finite element method (FEM) for static analysis of bar-type engineering structures involving FGM is presented. By extending the traditional support vector regression (SVR) method, a new kernel-based machine learning technique, namely the extended support vector regression (X-SVR), is proposed for modelling the underpinned relationship between the structural behaviours and the uncertain system inputs. The proposed structural reliability analysis inherits the advantages of the traditional sampling method (i.e., Monte-Carlo Simulation) on providing the information regarding the statistical characteristics (i.e., mean, standard deviations, probability density functions and cumulative distribution functions etc.) of any concerned structural outputs, but with significantly reduced computational efforts. Five numerical examples are investigated to illustrate the accuracy, applicability, and computational efficiency of the proposed computational scheme.

A virtual model aided non-deterministic static analysis (including linear and nonlinear analyses) with polyphase uncertainty is presented in this paper. Within an uncertain system, the polyphase uncertainty integrates both probabilistic and non-probabilistic uncertainties, which is more sophisticated than the conventional uncertainty modelling through a single type. To further improve the computational stableness and robustness of the virtual model, a kernel-based machine learning technique, namely Twin Extended Support Vector Regression (T-X-SVR), is newly developed. The feature of auto-learning is fulfilled through the Bayesian optimization. The proposed approach is capable of providing sufficient statistical information, including the membership functions of mean and standard deviation, fuzzy-valued probabilistic density function (PDF) and cumulative distribution function (CDF) for the upper and lower bounds of the concerned structural response. To demonstrate the effectiveness and computational efficiency of the proposed approach, a verification case, where analytical solutions are available, is tested first. Then, two practically stimulated engineering applications are fully investigated.

The nonlinear dynamic responses of a nanocomposite organic solar cell (NCOSC) are developed through the classical plate theory. The investigated NCOSC consists of five layers which are including Al, P3HT: PCBM, PEDOT: PSS, IOT and glass. A uniformly distributed external excitation is exerted on the simply supported NCOSC. The impacts of the Winkler-Pasternak elastic foundation, thermal environment and damping on the nonlinear dynamic responses of the NCOSC are investigated. The equations of motion and geometric compatibility of the NCOSC with the consideration of the von Karman nonlinearity are derived. The governing equation of the dynamic system is formulated by employing the Galerkin and the fourth-order Runge-Kutta methods. Several numerical experiments are thoroughly presented to report the effects of damping ratio, temperature variations, and elastic foundation parameters on the frequency–amplitude curves and nonlinear dynamic response of the NCOSC. The numerical studies indicate that the existence of the Winkler-Pasternak elastic foundation effectively reduces the dynamic response of the NCOSC. In addition, the damping and thermal variation depress the vibration of the NCOSC but with relatively less efficiency compared with the Winkler- Pasternak elastic foundation.

In this paper, a robust interval free vibration analysis for 3D functionally graded frame type engineering structure is presented through the finite element method (FEM). Uncertain material properties, which are including the Young's modulus and material density, of the functionally graded material are considered. Unlike the conventional uncertainty quantification through stochastic approach, the uncertain system inputs are modelled by the interval approach. Instead of straining on the precise statistical information of the uncertain parameters, only upper and lower bounds of the uncertain system inputs are required for valid structural safety assessment. By implementing the mathematical programming approach combined with the intrinsic characteristics of the non-deficient engineering structures, the upper and lower bounds of the natural frequencies of 3D functionally graded frame structure can be explicitly formulated by two independent eigen-problems. The sharpness and physical feasibility of the interval natural frequencies of the functionally graded structure can be well preserved. To demonstrate the competence of the proposed method, two numerical examples have been thoroughly investigated. In addition, diverse numerical investigations have been conducted to explore the impacts of uncertain material properties and the power-law index of the functionally graded materials on the overall structural performance.

© 2020 Elsevier B.V. A machine learning aided stochastic nonlocal damage analysis framework is proposed for quasi-brittle materials. The uncertain system parameters, including the material properties and loading actions, have been incorporated and analysed within a unified safety assessment framework against various working conditions. A three-dimensional integral-type nonlocal damage model through finite element method (FEM) has been adopted. For the purpose of investigating the probabilistic damage analysis problems, a freshly established machine learning approach, namely the capped-extended-support vector regression method (C-X-SVR), is proposed to eliminate the influences of random outliers in the first step, then establish the relationship between the uncertain systemic inputs and structural responses. Such that the training robustness and computational adaptability of the proposed regression model can be reinforced. Moreover, the proposed approach is competent of efficiently predicting the statistical information (i.e., means, standard deviations, probability density functions and cumulative density functions) of structural behaviours under continuous information update of the uncertain working condition from mercurial environment. One real-life experimental validation and two numerical investigations are implemented to further verify the effectiveness and efficiency of the uncertainty quantification framework against probabilistic damage analysis.

In this paper, we deal with the problem of finding the best possible bounds for the first Seiffert mean in terms of the geometric combination of logarithmic and the Neuman–Sándor means, and in terms of the geometric combination of logarithmic and the second Seiffert means.

A machine learning aided non-deterministic damage prediction framework against both 2D and 3D fracture problems is presented in this paper. By introducing a newly developed extended support vector regression (X-SVR) with generalized Dirichlet feature mapping into the phase field crack growth model, a damage assessment method that contains both crack diagnosis and prognosis is designed. Within the proposed analysis framework, the intricate fracture mechanism of practical engineering system can be learnt by the X-SVR model so a continuous damage diagnosis-prognosis loop can be established to assess the latest working condition of the structure. The proposed framework is applicable not only for quantifying and then assessing the current working conditions, but also for predicting the potentially crack propagation against the future forecasted information. Compared with the established experimental records and numerical result, the accuracy, effectiveness, and computational efficiency of the proposed framework are fully verified.

p,p’-dichlorodiphenyldichloroethylene (p, p’-DDE) and β-hexachlorocyclohexane (β-HCH) were two predominant organochlorine pesticides (OCPs) metabolites in human body associated with disorders of fatty acid metabolism. However, the underlying mechanisms have not been fully clarified. In this study, adult male C57BL/6 mice were exposed to low dose of p, p’-DDE and β-HCH for 8 wk. OCPs accumulation in organs, hepatic fatty acid composition, tricarboxylic acid cycle (TCA) metabolites and other metabolite profiles were analyzed. Expression levels of genes involved in hepatic lipogenesis and β-oxidation were measured. Mitochondrial function was evaluated in HepG2 cells exposed to OCPs. High accumulation of p, p’-DDE and β-HCH was found in liver and damaged mitochondria was observed under electron microscopy. Expression of genes in fatty acid synthesis increased and that in mitochondrial fatty acid β-oxidation decreased in OCPs treatment groups. OCPs changed metabolite profiles in liver tissues, varied hepatic fatty acid compositions and levels of several TCA cycle metabolites. Furthermore, MitoTracker Green fluorescence, ATP levels, mitochondrial membrane potential and OCR decreased in HepG2 cells exposed to OCPs. In conclusion, chronic exposure to OCPs at doses equivalent to internal exposures in humans impaired mitochondrial function, decreased fatty acid β-oxidation and aggravated disorders of fatty acid metabolism.

We present for the first time, to the best of our knowledge, the influence of spherical aberration on the beam quality of a single-stage laser amplifier. We set up an amplifier with a special structure to measure the spherical aberration distribution in the cavity. The output power of the oscillator was controlled, and the pump power of the amplifier was adjusted to improve the beam quality. The results show that there is an optimal amplifier pump power, which maximizes the output laser beam quality. In the presented experiment, an optimal laser beam was achieved with an output power of 10.54 W and beam quality of Mx2=1.54, My2=1.39 for a pump power of 9.33 W, oscillator output power of 9.2 W, and a beam quality of Mx2=2.10, My2=2.03.