A First-Order Transient Response Model for Lithium-ion Batteries of Various Chemistries: Test Data and Model Validation

In this report, a first-order transient response battery model is presented. The model can be utilized in the simulation of electric vehicles to calculate the battery voltage for dynamic operation of an electric-hybrid vehicle on various driving cycles. The battery model requires knowledge of the battery Ah capacity, the hyst-SOC-OCV curve and parameters of the equivalent circuit (R0, R1, tau1). A number of lithium-ion cells of the different chemistries were tested on charge / discharge step current profiles to determine the circuit parameters for a series of states-of-charge. The cells were then tested on the MHC and DST variable current profiles to determine how well the model predicted the response of the cells to the dynamic profiles. For DST test, the output voltages from the model for all the eight cells tested followed the test voltages well with the errors being relatively small –usually less than 20mV – except for SOC near to 1 and O. For MHC profile, the tests were performed at a nearly fixed SOC and the errors were particularly small. The study shows that over most of the useable state-of-charge range, the first-order transient model can be applied to predict the voltage response of lithium-ion batteries to dynamic charge and discharge currents encountered in vehicle applications

DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep Learning

Causal mediation analysis can unpack the black box of causality and is therefore a powerful tool for disentangling causal pathways in biomedical and social sciences, and also for evaluating machine learning fairness. To reduce bias for estimating Natural Direct and Indirect Effects in mediation analysis, we propose a new method called DeepMed that uses deep neural networks (DNNs) to cross-fit the infinite-dimensional nuisance functions in the efficient influence functions. We obtain novel theoretical results that our DeepMed method (1) can achieve semiparametric efficiency bound without imposing sparsity constraints on the DNN architecture and (2) can adapt to certain low dimensional structures of the nuisance functions, significantly advancing the existing literature on DNN-based semiparametric causal inference. Extensive synthetic experiments are conducted to support our findings and also expose the gap between theory and practice. As a proof of concept, we apply DeepMed to analyze two real datasets on machine learning fairness and reach conclusions consistent with previous findings.Comment: Accepted by NeurIPS 202

HEPATIC DIFFERENTIATION OF HUMAN AMNIOTIC EPITHELIAL CELLS AND IN VIVO THERAPEUTIC EFFECT ON ANIMAL MODEL OF CIRRHOSIS

Ph.DDOCTOR OF PHILOSOPH

Self-Esteem, Resilience, Social Support, and Acculturative Stress as Predictors of Loneliness in Chinese Internal Migrant Children: A Model-Testing Longitudinal Study

The present study examined the risk and protective factors of loneliness among Chinese internal migrant children (CIMC) in Beijing, China, including self-esteem, resilience, social support, and acculturative stress. Longitudinal survey data were collected from a large sample of 4th, 5th, and 6th grade CIMC from three schools in Beijing, at four time points (N=862 at T1 to N=837 at T4) over a 20-month period. Grounded in the Cultural and Contextual Model of Coping and the Acculturation Theory, two predictor models of loneliness were tested with path analysis. The results yielded the following: a) the two predictor models fit the data well; b) CIMC’s T1 self-esteem and T1 resilience protected them against loneliness at T4; and c) CIMC’s T2 social support seeking was a significant mediator between self-esteem and loneliness, and between resilience and loneliness; and d) similarly, CIMC’s T3 acculturative stress was a significant mediator between self-esteem and loneliness, and between resilience and loneliness. The study’s results highlight the merit and importance of implementing theoretically-guided, model-testing research grounded in a prospective research design, to help advance CIMC research. Implications for future research on and practical support for CIMC are discussed

A Dielectric Affinity Microbiosensor

We present an affinity biosensing approach that exploits changes in dielectric properties of a polymer due to its specific, reversible binding with an analyte. The approach is demonstrated using a microsensor comprising a pair of thin-film capacitive electrodes sandwiching a solution of poly(acrylamide-ran-3-acrylamidophenylboronic acid), a synthetic polymer with specific affinity to glucose. Binding with glucose induces changes in the permittivity of the polymer, which can be measured capacitively for specific glucose detection, as confirmed by experimental results at physiologically relevant concentrations. The dielectric affinity biosensing approach holds the potential for practical applications such as long-term continuous glucose monitoring

Reconstruction of compressed spectral imaging based on global structure and spectral correlation

In this paper, a convolution sparse coding method based on global structure characteristics and spectral correlation is proposed for the reconstruction of compressive spectral images. The proposed method uses the convolution kernel to operate the global image, which can better preserve image structure information in the spatial dimension. To take full exploration of the constraints between spectra, the coefficients corresponding to the convolution kernel are constrained by the norm to improve spectral accuracy. And, to solve the problem that convolutional sparse coding is insensitive to low frequency, the global total-variation (TV) constraint is added to estimate the low-frequency components. It not only ensures the effective estimation of the low-frequency but also transforms the convolutional sparse coding into a de-noising process, which makes the reconstructing process simpler. Simulations show that compared with the current mainstream optimization methods (DeSCI and Gap-TV), the proposed method improves the reconstruction quality by up to 7 dB in PSNR and 10% in SSIM, and has a great improvement in the details of the reconstructed image