Pre-service Chinese English as A Foreign Language (EFL) Teachers Perceptions about Implementation of Communicative Language Teaching

The government of China requires that the Communicative Language Teaching Approach (CLT) should be applied in primary and secondary school English education by issuing the New Curriculum early in 2001 to develop learners competence of using the language; however, implementation of CLT is still a big challenge confronting pre-service Chinese EFL teachers who experienced the traditional teaching approach over an extensive period of time. I conducted a case study research on the perceptions about CLT of twelve pre-service EFL teachers from Liaoning Province, China, to explore: a) what are pre-service Chinese EFL teachers\u27 perceptions about CLT and its implementation in the Chinese context? b) What are the important factors that affect their perceptions about CLT and its implementation? Vygotsky\u27s sociocultural theory was applied as the framework to examine the process of the participants\u27 perceptions. The study indicates that these pre-service EFL teachers considered CLT as an ineffective teaching approach for transferring linguistic knowledge, but they suggested implementing a small amount of communicative activities to relieve the repressed feelings of learners who learn English under the traditional teaching approach. The examination system, the previous English learning experience, the internalized Chinese culture of learning, and the pre-existing beliefs of teaching are the important factors influencing how these pre-service teachers were aware of, understood, interpreted, and emotionally related to English teaching using a communicative approach. These pre-service EFL teachers developed new beliefs of teaching in the process of training; however, lack of role models in the local educational realities prevented them from applying and creating communicative methods. Therefore, I suggest that teacher educators incorporate reflective discussions and demonstrate various implementations of CLT in EFL teacher-training programs.\u2

Detecting Biomarkers among Subgroups with Structured Latent Features and Multitask Learning Methods

University of Minnesota Ph.D. dissertation. May 2017. Major: Computer Science. Advisor: Rui Kuang. 1 computer file (PDF); viii, 89 pages.Because of disease progression and heterogeneity in samples and single cells, biomarker detection among subgroups is important as it provides better understanding on population genetics and cancer causative. In this thesis, we proposed several structured latent features based and multitask learning based methods for biomarker detection on DNA Copy-Number Variations (CNVs) data and single cell RNA sequencing (scRNA-seq) data. By incorporating prior known group information or taking domain heterogeneity into consideration, our models are able to achieve meaningful biomarker detection and accurate sample classification. 1. By cooperating population relationship from human phylogenetic tree, we introduced a latent feature model to detect population-differentiation CNV markers. The algorithm, named tree-guided sparse group selection (treeSGS), detects sample sub- groups organized by a population phylogenetic tree such that the evolutionary relations among the populations are incorporated for more accurate detection of population- differentiation CNVs. 2. We applied transfer learning technic for cross-cancer-type CNV studies. We proposed Transfer Learning with Fused LASSO (TLFL) algorithm, which detects latent CNV components from multiple CNV datasets of different tumor types and distinguishes the CNVs that are common across the datasets and those that are specific in each dataset. Both the common and type-specific CNVs are detected as latent components in matrix factorization coupled with fused LASSO on adjacent CNV probe features. 3. We further applied multitask learning idea on scRNA-seq data. We introduced variance-driven multitask clustering on single-cell RNA-seq data (scV DMC) that utilizes multiple cell populations from biological replicates or related samples with significant biological variances. scVDMC clusters single cells of similar cell types and markers but varies expression patterns across different domains such that the scRNA-seq data are adjusted for better integration. We applied both simulations and several publicly available CNV and scRNA-seq datasets, including one in house scRNA-seq dataset, to evaluate the performance of our models. The promising results show that we achieve better biomarker prediction among subgroups

Twins: Scalable 2-Hop Structured Overlay Network

In this paper we propose a new structured overlay network, which is more efficient and scalable than previous ones. We call it Twins, because its routing table consists of two parts, one containing nodes with common prefix and the other containing nodes with common suffix. Twins routes messages to their destinations in just 2 hops even in a very large scale and the overhead is very low. When deployed in a peer-to-peer system with 5 000 000 nodes, each node receives only 6 messages per second for routing table maintenance. This cost, as well as routing table size, varies as a O(sqrt N) function to the overlay scale, so Twins can also run well in an even larger environment

Layered Nanocomposite for Neural Prosthetic Devices.

The motivation of this dissertation is developing neural prosthetic devices for chronic brain-computer interface. To maintain a chronically sustainable brain-computer interface, implantable devices should have minimal chronic inflammation, mechanically compliance with neural tissue, and long term durability under physiological conditions. Traditional neural prosthetic devices can seldom fulfill these requirements. This dissertation presents a nanocomposite approach to design the next generation neural prosthetic devices. The rationally designed nanocomposite can achieve the combination of the desired material properties for neural prosthetic devices, currently unattainable by traditional materials. In this dissertation, we first fabricated a mechanically compliant neural electrode from carbon nanotubes. The seamless integration of carbon nanotubes and polymer offered both mechanical flexibility and electrical conductivity for neural prosthetic devices. Then we explored other nanomaterials to design more exceptional nanocomposites. The gold nanoparticle nanocomposite developed in this research outperformed carbon nanotube composite in term of electrochemical performance. Additionally, we utilized the nanocomposite approach to design flexible insulation material for implantable electronic. By combining aramid nanofibers and epoxy resin, the composite material has outstanding adhesion and biocompatibility. Lastly, we designed a microfabrication process to combine gold nanoparticle composite and aramid nanofiber composite to create tissue compliant and high performance neural electrodes.PHDChemical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/107222/1/runhrun_1.pd

Data-Driven Algorithms for Stochastic Supply Chain Systems: Approximation and Online Learning

In the era of Big Data, with new and emerging technologies, data become easily attainable for companies. However, acquiring data is only the first step for the company. The second and more important step is to effectively integrate the data through the learning process (mining the data) in the decision-making process, and to utilize the information extracted from data to improve the efficiency of the company’s supply chain operation. The primary focus of this dissertation is on multistage stochastic optimization problems arising in the context of supply chains and inventory control problems, and on the design of efficient algorithms to solve the respective models. This dissertation can be categorized into two broad areas as follows. The first part of this dissertation focuses on the design of non-parametric learning algorithms for complex inventory systems with censored data. We address two challenging stochastic inventory control models: the periodic-review perishable inventory system and the periodic-review inventory control problem with lost-sales and positive lead times. We assume that the decision maker has no demand distribution information available a priori and can only observe past realized sales (censored demand) information to optimize the system's performance on the fly. For each of the problems, we design a learning algorithm that can coverage to the best base-stock policy with tight regret rate. The second part of this dissertation focuses on the design of approximation algorithms for stochastic perishable inventory systems with correlated demand. In this part, we consider the perishable inventory system from the optimization perspective. Different from traditional perishable inventory literature, we allow demands to be arbitrarily correlated and nonstationary, which means we can capture the seasonality nature of the economy, and allow the decision makers to effectively incorporate demand forecast. For this problem, we develop two approximation algorithms with worst-case performance guarantees. Through comprehensive numerical experiments, we have shown that the numerical performances of the approximation algorithms are very close to optimal.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138697/1/zhanghn_1.pd

Stochastic regret minimization for revenue management problems with nonstationary demands

We study an admission control model in revenue management with nonstationary and correlated demands over a finite discrete time horizon. The arrival probabilities are updated by current available information, that is, past customer arrivals and some other exogenous information. We develop a regret‐based framework, which measures the difference in revenue between a clairvoyant optimal policy that has access to all realizations of randomness a priori and a given feasible policy which does not have access to this future information. This regret minimization framework better spells out the trade‐offs of each accept/reject decision. We proceed using the lens of approximation algorithms to devise a conceptually simple regret‐parity policy. We show the proposed policy achieves 2‐approximation of the optimal policy in terms of total regret for a two‐class problem, and then extend our results to a multiclass problem with a fairness constraint. Our goal in this article is to make progress toward understanding the marriage between stochastic regret minimization and approximation algorithms in the realm of revenue management and dynamic resource allocation. © 2016 Wiley Periodicals, Inc. Naval Research Logistics 63: 433–448, 2016Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135128/1/nav21704.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135128/2/nav21704_am.pd

Research Progress on the Formation Mechanism of Protein/Essential Oil-based Composite Films and Application in Food Preservation

It has great significance to maintain the natural state of food and reduce the environmental pollution that use of bio-based packaging materials to research and development food packaging, because it has been unable to meet the demand of consumers for green, environmental protection and high-efficiency, for traditional food packaging. Edible films are attracting great attentions in food packaging due to their safety and zero waste property, among which, the protein often used in the preparation of edible films because of good mechanical properties and nutritional value. Essential oils extracted from aromatic plants, the broad-spectrum antibacterial and antioxidant properties give it great potential in food packaging. Incorporation of essential oils into edible protein-based films can effectively improve their properties, and cover the strong sensory properties of essential oils, meanwhile, the release rate of essential oils is controlled. This review covers the recent developments in protein/essential oils-based composite films, the formation mechanism of protein/essential oils-based composite films is discussed, the two composite methods of single essential oil and complex essential oil with protein-based composite films is introduced. Meanwhile, the application of protein/essential oils-based composite films in food is summarized. This study can provide some reference for the future development of protein/essential oils-based composite films

Enzyme-Catalytic Self-Triggered Release of Drugs from a Nanosystem for Efficient Delivery to Nuclei of Tumor Cells.

Stimulus-responsive drug delivery nanosystems (DDSs) are of great significance in improving cancer therapy for intelligent control over drug release. However, among them, many DDSs are unable to realize rapid and sufficient drug release because most internal stimulants might be consumed during the release process. To address the plight, an abundant supply of stimulants is highly desirable. Herein, a core crosslinked pullulan-di-(4,1-hydroxybenzylene)diselenide nanosystem, which could generate abundant exogenous-stimulant reactive oxygen species (ROS) via tumor-specific NAD(P)H:quinone oxidoreductase-1 (NQO1) catalysis, was constructed by the encapsulation of β-lapachone. The enzyme-catalytic-generated ROS induced self-triggered cascade amplification release of loaded doxorubicin (DOX) in the tumor cells, thus achieving efficient delivery of DOX to the nuclei of tumor cells by breaking the diselenide bond of the nanosystem. As a result, the antitumor effect of this nanosystem was significantly improved in the HepG2 xenograft model. In general, this study offers a new paradigm for utilizing the interaction between the loaded agent and carrier in the tumor cells to obtain self-triggered drug release in the design of DDSs for enhanced cancer therapy