Polynomial Chaos Expansion for Probabilistic Uncertainty Propagation

Uncertainty propagation (UP) methods are of great importance to design optimization under uncertainty. As a well-known and rigorous probabilistic UP approach, the polynomial chaos expansion (PCE) technique has been widely studied and applied. However, there is a lack of comprehensive overviews and studies of the latest advances of the PCE methods, and there is still a large gap between the academic research and engineering application for PCE due to its high computational cost. In this chapter, latest advances of the PCE theory and method are elaborated, in which the newly developed data-driven PCE method that does not depend on the complete information of input probabilistic distribution as the common PCE approaches is introduced and improved. Meanwhile, the least angle regression technique and the trust region scenario are, respectively, extended to reduce the computational cost of data-driven PCE to accommodate it to practical engineering design applications. In addition, comprehensive comparisons are made to explore the relative merits of the most commonly used PCE approaches in the literature to help designers to choose more suitable PCE techniques in probabilistic design optimization

Causal connectivity abnormalities of regional homogeneity in children with attention deficit hyperactivity disorder: a rest-state fMRI study

The present study aimed to investigate individual differences of causal connectivity between brain regions in attention deficit hyperactivity disorder (ADHD) which was a psychiatric disorder. Resting-state functional magnetic resonance imaging (R-fMRI) data of typically-developing controls (TDC) children group and combined ADHD (ADHD-C) children group were distinguished by the support vector machine (SVM) with linear kernel function, based on regional homogeneity (ReHo), amplitude of low frequency fluctuation (ALFF) and fractional ALFF (FALFF). The highest classification accuracy yielded by ReHo was 90.91 %. Furthermore, the granger causality analysis (GCA) method based on the classified weight map of regions of interesting (ROIs) showed that five causal flows existed significant difference between TDC and ADHD-C. That is, the averaged GCA values of three causal connections (i.e. left VLPFC left CC1, right PoCG left CC1, and right PoCG right CC2) for ADHD-C were separately stronger than those for TDC. And the other two connections (i.e. right FEF right SOG and right CC1 right SOG) were weaker for ADHD-C than those for TDC. In addition, only two causality flows (i.e. left VLPFC left CC1 and right PoCG right CC2) presented that their GCA values were positively correlation with ADHD index scores, respectively. Our findings revealed that ADHD children represented widespread abnormalities in the causality connectivity, especially involved in the attention and memory related regions. And further provided evidence that the potential neural causality flows could play a key role in characterizing individual’s ADHD

The efficacy of the Hiline gas permeable contact lens for the management of Keratoconus

Purpose: To investigate the efficacy of the Hiline® gas permeable contact lens (Hiline® GP) for keratoconus in clinical practice in correcting visual acuity. Methods: 218 eyes of 126 patients with keratoconus were fitted with Hiline® lenses. The fit of the lenses was evaluated. Visual acuity measurements were taken with spectacle lenses and with the Hiline lenses. The period of follow-up to observe for complications ranged from 3 to 27 months. Results: In all eyes, the Hiline® GP provided acceptable vision. There was a statistically significant improvement in vision with the Hiline® GP compared with spectacle lenses (t=10.90, p<0.0001). Initial evaluation showed that 169 lenses (77.52%) demonstrated a three-pointtouch relationship with the cornea 38 lenses (17.43%) had an apical clearance relationship with the cornea and 11 lenses (5.05%) had an apical bearing relationship. No severe complications were observed. Conclusions: Using corneal topography as a guide, a high success rate was achieved with the Hiline® GP design. It is easy to reach the ideal fit and to improve the visual acuity. These indicate the usefulness of Hiline® lens in clinical practice

Explore postgraduate biomedical engineering course integration between medical signal processing and drug development: example for drug development in brain disease

Medical signal processing is a compulsory course in our university’s undergraduate biomedical engineering programme. Recently, application of medical signal processing in supporting new drug development has emerged as a promising strategy in neurosciences. Here, we discuss the curriculum reformation in biomedical signal processing course in the context of drug development and application in central nervous system, with a particular emphasis in knowledge integration

Identifying the Best Candidate for Primary Tumor Resection in Patients With Advanced Osteosarcoma

Objectives Not all patients with stage III and IV osteosarcoma who undergo surgery to remove the primary tumor will benefit from surgery; therefore, we developed a nomogram model to test the hypothesis that only a subset of patients will benefit from surgery. Methods 412 patients were screened from the Surveillance, Epidemiology and End Results (SEER) database. Subsequently, 1:1 propensity score matching (PSM) was used to screen and balance confounders. We first made the hypothesis that patients who underwent the procedure would benefit more. A multivariate Cox model was used to explore the independent influencing factors of CSS in two groups (benefit group and non-benefit group) and constructed nomograms with predicted prognosis. Finally, receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to verify the performance of the nomogram. Results Of these patients, approximately 110 did not undergo primary tumour resection. After passing PSM, they were divided into a surgical group and a non-surgical group. Age, primary site and chemotherapy as calculated independent factors were used to construct a nomogra. The predicted nomogram showed good consistency in terms of the ROC curve and the calibration curve, and the DCA curve showed a certain clinical utility. Finally, dividing the surgical patients into surgical beneficiaries and surgical non-beneficiaries, a Kaplan–Meier analysis showed that the nomogram can identify patients with osteosarcoma who can benefit from surgery. Conclusion A practical predictive model was established to determine whether patients with stage III or IV osteosarcoma would benefit from surgery

Study on the chaotic dynamics in yaw-pitch-roll coupling of asymmetric rolling projectiles with nonlinear aerodynamics

To predict the coning motion forms of a rolling projectile with configurational asymmetries, the nonlinear characteristics for the system are investigated in this paper. The nonlinear dynamic model of rolling projectiles in coning motion is built by considering the nonlinear aerodynamics and roll orientation-dependent aerodynamics. The configurational asymmetry is modeled as a periodically parametric excitation in order to study its effect on the periodic response stability of the rolling projectile. Numerical continuation method is resorted to determine the parametric zone for the steady motions, and the possible stable rotational speeds are discussed. The numerical simulations, Lyapunov exponent spectrum analysis and Poincare sections are performed to confirm the existence of chaotic coning motion. The results shown in this study not only contribute to an in-depth understanding for the nonlinear dynamics of rolling projectiles but also provide an important reference for the further study of the control design for the yaw-pitch-roll coupling of asymmetric rolling projectiles with nonlinear aerodynamics

Facial Expression Transfer Based on Conditional Generative Adversarial Networks

With the development of computer vision and image transfer, facial expression transfer has been more and more widespread applications. But there are still some problems, such as lack of realistic expression, poor retention of facial identity features and low synthesis efficiency. In order to solve the problems of facial expression transfer, the paper proposes a facial expression transfer model based on conditional generative adversarial network, which can generate a highly realistic face image with source facial expression and target facial identity features, when gave a source face image and a target face image. The model consists of two parts: the facial feature point fusion module and the expression transfer module. Among them, the facial feature point fusion module uses an auto-encoder to encode the face key feature point image of the source facial expression and the face feature key point image of the target face, so as to transfer the source facial expression information to the corresponding face key feature points of the target image; the expression transfer module uses the facial feature point fusion module to generate the face key feature point image and the target face image, and then generates an image with the source facial expression and the target face identity features through the modified U-net network. The model is finally validated on two publicly available datasets, RaFD and CK+, and the experimental results show that the generated facial expression is more realistic than the pix2pix model, and the model only needs to be trained once to complete the transfer between any facial expression