Live Birth Bias In Epidemiological Study Of Timing Specific Exposure Effect During Pregnancy And Child Health: A Simulation Study

In reproductive and perinatal epidemiological studies, measurement of child health outcomes that can only be ascertained in live born children may be incomplete since only 60 – 70% of fertilized eggs result in live births and early pregnancy loss is often undetected. Studies assessing outcomes among live born children are subject to live birth bias, a phenomenon previously proposed as a form of collider-bias in which conditioning on live-birth status induces a non-causal association between exposure and outcome. In this study, we expanded a previously proposed common structure of this bias to evaluate its impact on the estimation of time-specific prenatal exposure effects on child health outcome, using causal diagrams. We used Monte Carlo simulation techniques to investigate two scenarios in which prenatal exposures led to pregnancy loss. Our findings confirmed previous simulation findings showing biased estimates of prenatal exposure effects on child outcome risk, assuming a true null association between each exposure and the outcome and using trimesters to characterize the exposure timing. We observed larger bias sizes when the effect size of the exposure-fetal survival relations increased and/or when other unmeasured and uncontrolled risk factors had stronger effect on both fetal survival and the outcome. Our study underlines the needs for the development of analytic methods that adjust for live birth bias in scenarios accounting for time-specific exposure effects and time-specific selections

Bi-level iterative regularization for inverse problems in nonlinear PDEs

We investigate the ill-posed inverse problem of recovering unknown spatially dependent parameters in nonlinear evolution PDEs. We propose a bi-level Landweber scheme, where the upper-level parameter reconstruction embeds a lower-level state approximation. This can be seen as combining the classical reduced setting and the newer all-at-once setting, allowing us to, respectively, utilize well-posedness of the parameter-to-state map, and to bypass having to solve nonlinear PDEs exactly. Using this, we derive stopping rules for lower- and upper-level iterations and convergence of the bi-level method. We discuss application to parameter identification for the Landau-Lifshitz-Gilbert equation in magnetic particle imaging

THE RELATIONSHIP BETWEEN BILINGUALISM AND EMOTION PERCEIVED BY VIETNAMESE COLLEGE STUDENTS

Studies of the correlation between language and emotion have demonstrated the existence of a causal relationship between switching languages and feeling different in bi-/ multilinguals. Adopting the mixed-method approach, the current research aims to extend this line of enquiry to Vietnam – a monolingual country – by investigating 160 Vietnamese-English speaking students at International University (IU) (VNU-HCMC). They were required to complete a questionnaire based on the Bilingualism and Emotion Questionnaire (Dewaele & Pavlenko, 2001–2003). It includes closed questions concerning shifts on five scales of feelings and an open explanatory question on the difference perceived. The scales, chosen with reference to the research of Dewaele and Nakano (2012), consist of feeling logical, serious, emotional, fake, and different. Statistical analyses revealed a regular shift on most scales, with most participants feeling more logical, more serious, more fake, more different, and less emotional when using the L2. Simple linear regression revealed that the variation in certain feelings scales was mostly predicted by self-perceived proficiency in the L

Discretization of parameter identification in PDEs using Neural Networks

We consider the ill-posed inverse problem of identifying a nonlinearity in a time-dependent PDE model. The nonlinearity is approximated by a neural network, and needs to be determined alongside other unknown physical parameters and the unknown state. Hence, it is not possible to construct input-output data pairs to perform a supervised training process. Proposing an all-at-once approach, we bypass the need for training data and recover all the unknowns simultaneously. In the general case, the approximation via a neural network can be realized as a discretization scheme, and the training with noisy data can be viewed as an ill-posed inverse problem. Therefore, we study discretization of regularization in terms of Tikhonov and projected Landweber methods for discretization of inverse problems, and prove convergence when the discretization error (network approximation error) and the noise level tend to zero

INNOVATION OF TRAINING METHODS TO MEET THE NEEDS OF UNIVERSITY STUDENTS UNDER "LEARNING CITIZEN" MODEL

After reflecting on the situation, the paper focuses on analyzing the innovation of higher education methods to meet learners' needs under the "learning citizen" model in the context of approaching the Fourth Industrial Revolution, with many opportunities and challenges. The paper identifies that higher education innovation to meet learners' needs under the "learning citizen" model is an effective way to take care of people's spiritual and material life, which is an important contribution to build a learning society. Article visualizations