Controlled Manipulation of Droplets on Fibers: Fundamentals and Printing Applications

In this dissertation, the drop interactions with a single fiber is discussed under an application angle for the development on new Drop-on-Demand (DOD) printhead using a fiber-in-a-tube platform[1] to print highly viscous materials[2]. To control the drop formation and manipulation on fiber, one needs to know how the fiber wetting properties and the fiber diameter influence drop formation. And then, one needs to know the effects of fiber movement in the device on drop formation. These two questions constitute the main theme of this dissertation. Before this study, it was accepted that the liquids could not form axisymmetric droplets if the liquid drop makes the contact angle greater than 90 degrees on a flat substrate of the same material. In Chapter 2, all possible configurations of an axisymmetric drop wrapping up the fiber were analyzed rigorously by studying all solutions of the Laplace equation of capillarity for small droplets for which gravity is insignificant. In Chapter 3, an experimental analysis of morphological transitions of droplet configurations has been systematically conducted. When the droplets are large and are able to wrap up the fiber, they form barreled configurations; when the volume of droplets is small, the barrels cannot be formed and droplets rest as clamshells on the fiber side. With these analyses in hands, one can design of a fiber-in-a-tube printhead taking advantage of the established diagrams for formation of barreled droplets. Drop-on-demand (DOD) printing is a versatile manufacturing tool, which has been widely used in applications ranging from graphic products to manufacturing of ceramics, even for cell engineering. However, the existing DOD methods cannot be applied for highly viscous materials: the printing technologies are typically limited to the inks with the water level viscosity and fall short of ejecting jets from thick fluids and breaking them into droplets. To address this challenge, a new wire-in-a-tube technology for drop generation has been developed replacing the nozzle generator with a wire-in-a-tube drop generator. In Chapter 4, we introduce the wire-in-a-tube generator and show successful printing results of droplets on-demand from highly viscous (~10 Pa*s) liquids. In Chapter 5, we study the drop formation mechanisms in the wire-in-a-tube drop generators. These mechanisms couple unique fluid mechanics, capillarity, and wetting phenomena providing a new platform that can be used in different microfluidic applications

Recommendation in Dialogue Systems

Dialogue system has been an active research field for decades and is developing fast in recent years, due to the recent breakthrough of the deep learning techniques. How to make recommendations in dialogue systems is attracting increasing attention because such systems could meet various user information needs and have much commercial potential.Current dialogue system researches typically focus on building systems for social conversation, question answering, and performing specific tasks. However, making recommendations to users, as important information need, has not been intensively researched. Meanwhile, traditional recommender systems are usually developed for non-conversation scenarios. In this dissertation, we explore how to integrate these two systems into one framework that specifically aims at making recommendations in dialogues. Such a system helps users find items by chatting with users to understand their preferences and recommending accordingly.First, we build conversational recommendation datasets, because existing dialogue datasets do not have user-item preference information or the dialogue utterances discussing facets of items, and current recommendation datasets do not have dialogue scripts associated with each user-item pair. We build the datasets by requesting crowdsourcing workers to compose dialogue utterances based on schemas and then use the delexicalization approach to simulate dialogues with the collected utterances. The datasets are used to train the natural language understanding component and provide recommendation information for our system.Based on collected datasets, we propose a reinforcement learning based conversational recommendation framework. Such a framework has three components, a belief tracker, a dialogue manager, and a recommender. The dialogue agent learns to first chat with a user to understand her preferences, and when it feels confident enough, it recommends a list of items to the user. We conduct both offline and online experiments to demonstrate the effectiveness of the framework.We further extend this framework with a personalized probabilistic recommender module. This recommender learns to predict the probability of a user likes an item given the dialogue utterance information and the personalized user preference information. By leveraging this hybrid information, the recommendation and dialogue performances are further improved. We evaluate the dialogue agent's strength in various simulated environments as well as in online user studies and demonstrate the advantages of this approach

Characterization of the fertilization independent endosperm (FIE) gene from soybean

Reproduction of angiosperm plants initiates from two fertilization events: an egg fusing with a sperm to form an embryo and a second sperm fusing with the central cell to generate an endosperm. The tryptophan-aspartate (WD) domain polycomb protein encoded by fertilization independent endosperm (FIE) gene, has been known as a repressor of hemeotic genes by interacting with other polycomb proteins, and suppresses endosperm development until fertilization. In this study, one Glycine max FIE (GmFIE) gene was cloned and its expression in different tissues, under cold and drought treatments, was analyzed using both bioinformatics and experimental methods. GmFIE showed high expression in reproductive tissues and was responsive to stress treatments, especially induced by cold. GmFIE overexpression lines of transgenic Arabidopsis were generated and analyzed. Delayed flowering was observed from most transgenic lines compared to that of wild type. Overexpression of GmFIE in Arabidopsis also leads to semi-fertile of the plants.Keywords: Polycomb proteins, fertilization independent endosperm (FIE), Glycine max, Arabidopsis thalian

Effects of self-healing biomimetic subsoiler on tillage resistance, wear-corrosion performance and soil disturbance morphology under different soil types

Subsoiling has been widely used all over the world as an important operation method of no-tillage farming. For energy-saving and life-extension, the tillage resistance and wear-corrosion of subsoilers have attracted wide attention. In this study, the tillage resistance, soil disturbance, wear and corrosion of subsoiler with S-T-SK-2# biomimetic structures (S means subsoiler; T means tine; SK means shank; 2#, h/s=0.57, h=5 mm and α=45°.) and self-healing coating under two seasons, two locations with different soil properties (black loam and clay soil) and subsoiling speeds (2 km/h and 3.6 km/h) were investigated. The soil moisture content and compactness affected the tillage resistance and wear-corrosion. The tillage resistance and degree of corrosion on all subsoilers were much larger in clay soil than that in black loam soil. Compared with S-T-SK-2#, the tillage reduction rate of C-S-T-SK-2# (S-T-SK-2# with self-healing coating) was up to 14.32% in clay soil under the speed of 2 km/h. The significance tests of regression equation results showed that subsoiler type and soil properties had a significant impact on soil disturbance coefficient, swelling of total soil layer, bulkiness of the plough pan. It is of a guiding significance for the analysis of soil disturbance. Synergism mechanism of subsoiler coupling with biomimetic structures and self-healing coating was analyzed in following. It depicted the guiding effect of biomimetic structure and the shield function of self-healing coating, resulting in anticorrosion and wear resistance of subsoiler

Learning to Ask: Question-based Sequential Bayesian Product Search

Product search is generally recognized as the first and foremost stage of online shopping and thus significant for users and retailers of e-commerce. Most of the traditional retrieval methods use some similarity functions to match the user's query and the document that describes a product, either directly or in a latent vector space. However, user queries are often too general to capture the minute details of the specific product that a user is looking for. In this paper, we propose a novel interactive method to effectively locate the best matching product. The method is based on the assumption that there is a set of candidate questions for each product to be asked. In this work, we instantiate this candidate set by making the hypothesis that products can be discriminated by the entities that appear in the documents associated with them. We propose a Question-based Sequential Bayesian Product Search method, QSBPS, which directly queries users on the expected presence of entities in the relevant product documents. The method learns the product relevance as well as the reward of the potential questions to be asked to the user by being trained on the search history and purchase behavior of a specific user together with that of other users. The experimental results show that the proposed method can greatly improve the performance of product search compared to the state-of-the-art baselines.Comment: This paper is accepted by CIKM 201

An exotic fruit with high nutritional value: Kadsura coccinea fruit

This research was to determine nutritional composition, essential and toxic elemental content, and major phenolic acid with antioxidant activity in Kadsura coccinea fruit. The results indicated that Kadsura coccinea fruit exhibited the high contents of total protein, total fat, ash and essential elements such as calcium (Ca), ferrum (Fe) and phosphorus (P). The levels of four common toxic elements, i.e. cadmium (Cd), mercury (Hg), arsenic (As) and lead (Pb), were lower than legal limits. By high-performance liquid chromatography (HPLC) analysis, gallic acid was identified as major phenolic acid in peel and pulp tissues. Its contents were no significant difference in both tissues. In comparison with two commercial antioxidants, the major phenolic acid extracted from Kadsura coccinea exhibited stronger 1,1-diphenyl-2-picrylhydrazyl radical-scavenging activity and reducing power. Kadsura coccinea fruit is a good source of nutrition and natural antioxidant. It is worthwhile to popularize this exotic fruit around the world

Financial integration of NAFTA : measurement and analysis of the North American financial markets convergence / Yueming (Roy) Sun

vii, 67 leaves ; 29 cmApplying market arbitrage theory on daily data, we measure the empirical financial market convergence of NAFTA’s financial markets since 1994. Radar diagram and wavelet multi-resolution analysis (MRA) scalogram movies of the statistical moments of the term interest rate differentials visualize the multidimensional convergence. From the radar movies, we find: 1) a uniform disappearance of the average forward premia; 2) a non-uniform decline of bilateral financial market risk; 3) variation of bilateral financial market pressure measured by skewness; and 4) emergence of uniform market microstructures as measured by vanishing excess-kurtosis. From the MRA movies, we find that the national term structures of interest rates converge, since the stochastic resonance coefficients of the interest rate differentials lose significance: market energy at all frequencies dissipates into “white noise.” Testing Obrimah, Prakash and Rangan’s (2009) Lemma, we find that, after 2002, higher financial flow pressure is a necessary condition for lower financial market risk