A Multi-functional Touch Panel for Multi-Dimensional Sensing in Interactive Displays

This thesis presents a flexible graphene/polyvinylidene difluoride (PVDF)/graphene sandwich for three-dimensional touch interactivity. Here, an x-y plane touch is sensed using graphene capacitive elements, while force sensing in the z-direction is by a piezoelectric PVDF/graphene sandwich. By employing different frequency bands for the capacitive- and force-induced electrical signals, the two stimuli are detected simultaneously, achieving three-dimensional touch sensing. Static force sensing and elimination of propagated stress are achieved by augmenting the transient piezo output with the capacitive touch, thus overcoming the intrinsic inability of the piezoelectric material in detecting non-transient force signals and avoiding force touch mis-registration by propagated stress. As a capacitive signal is important for force touch interpretation, optimization algorithms have been developed and implemented. With correlated double sampling (CDS) and spatial low-pass filtering (SLPF) based techniques, the signal-to-noise ratio (SNR) of the capacitive touch signal is boosted by 15.6 dB, indicating improved detection accuracy. In terms of the readout speed, fixed pattern and random pattern related down-sampling techniques are applied, giving rise to reductions in both readout time (11.3 ms) and power consumption (8.79 mW).China Scholarship Counci

Functional-Material-Based Touch Interfaces for Multidimensional Sensing for Interactive Displays: A Review

Multidimensional sensing is a highly desired attribute for allowing human-machine interfaces (HMIs) to perceive various types of information from both users and the environment, thus enabling the advancement of various smart electronics/applications, e.g., smartphones and smart cities. Conventional multidimensional sensing is achieved through the integration of multiple discrete sensors, which introduces issues such as high energy consumption and high circuit complexity. These disadvantages have motivated the widespread use of functional materials for detecting various stimuli at low cost with low power requirements. This work presents an overview of simply structured touch interfaces for multidimensional (x-y location, force and temperature) sensing enabled by piezoelectric, piezoresistive, triboelectric, pyroelectric and thermoelectric materials. For each technology, the mechanism of operation, state-of-the-art designs, merits, and drawbacks are investigated. At the end of the article, the author discusses the challenges limiting the successful applications of functional materials in commercial touch interfaces and corresponding development trends

A Comparative Study of Sino-U.S. Business Negotiation Strategy From the Perspective of Cultural Dimensions Theory

Business negotiation serves as an important activity in Sino-U.S. trade where Chinese companies pay much attention to the relations with their American counterparts. Due to the salient differences in cultures and ways of doing business, negotiating conflicts occur frequently, which impedes the smooth advance of business activities. This comparative research aims to analyze differences in Sino-U.S. business negotiation from an intercultural perspective, providing advice for Chinese negotiators in an attempt to reduce misunderstandings and disputes. The author has collected information about the definition of international negotiation as well as the current state of intercultural research and summarized previous related studies. This study employs Hofstede’s cultural dimensions theory and conducts case analysis in ways that apply the theory into practical negotiation situation.The findings show that Chinese negotiators value long-term business partnership; in addition, they often consult their superiors when the expected conditions change; in terms of communication model, Chinese negotiators prefer indirect speech and constantly use euphemism; a general framework on the contract is more important than specific details for them. American negotiators give priority to the realization of business goals; negotiators represent the company to make decisions and are responsible for the negotiation results; Americans often point out issues face to face and specify concrete solutions to problems; compared with Chinese negotiators, they prefer to reach a consensus on detailed matters and stress less on general tenets. This study illustrates features of Sino-U.S. negotiation in an attempt to provide guidance for future related studies. The author also tries to summarize some pragmatic strategies for Chinese negotiators so as to facilitate the negotiation

On Wang Rongpei’s Drama Translation Strategy: A Case Study of The Peony Pavilion

As a special literary form, drama is characterized by personalization, colloquialization, rhythm and the feature of performability. For a long time, the focus of drama translation studies has also fallen on the “performability” at home and abroad. The Peony Pavilion is one of the masterpieces in ancient China. With its beautiful and elegant lyrics, engaging anecdotes and vivid characters, The Peony Pavilion has an enduring popularity on the opera stage and a high literary value. There are many translations of The Peony Pavilion. Among all the versions, Wang Rongpei’s translation has balanced “spirit” and “meaning” and creatively reproduced the original style.Wang Rongpei first proposed the translation theory of “faithful in meaning and vivid in description” when he translatingThe Book of Songs in 1994. He thought that “faithful” refers to accurately express the meaning of the original text with target language, and “vivid” means showing the original style, emotion, rhythm, images, and characteristics. This article takes Wang Rongpei’s version of The Peony Pavilion as the study object and puts it under the criteria of “faithful in meaning and vivid in description”, analyzing the translation of the lyrics and researching how he transmits the spirit on the basis of being faithful in meaning. The aim of this article is to analyze Wang Rongpei’s dramatic translation techniques and explore the guiding and theoretical significance of this strategy for Chinese classical opera translation

Modelling and Forecasting Methods in Financial Economics

This dissertation consists of three chapters. Chapter 1: Behavioral heterogeneity among investors has been shown to explain the volatile nature of stock markets. In this chapter, I investigate the different behaviors of investors by proposing a heterogeneous agent model based on Chiarella et al. (2012) which involves fundamentalists, chartists, and noise traders with two-state hidden-Markov regime switching expectations. By applying the S&P 500 and CPI data from January 1990 to December 2020, the model shows strong evidence of behavioral heterogeneity among different groups of traders. After an in-sample backtesting and out-of-sample forecasting which further evaluate the capability of the model, two simple trading strategies are designed, both of which imply that the trading performance is better than the S&P 500 index. Chapter 2: Stock price movement prediction is challenging among researchers because of non-stationary nature of the data. In recent years, machine learning models have become increasingly popular in predicting stock markets. In this chapter, using training data from 01/2012 to 12/2017 and test data from 01/2018 to 12/2018, I predict the daily and weekly average price movement of S&P 500 constituents and compare the prediction accuracy using five machine learning models: Artificial neural network, Naive Bayes classifier, Support vector classifier ensemble, Random forest, and Boosted decision trees. For the input features of each stock, LASSO penalized logistic regression is performed to extract the top 5 features from all the 65 technical and macroeconomic indicators. Experimental results show that Naive Bayes classifier outperforms other models, and weekly average price is more predictable than daily price. Chapter 3: Yield curve modelling and forecasting is crucial for investment management. Using monthly yield curve data from 2003 to 2021, I investigate the forecasting performance of the yield curve using dynamic Nelson-Siegel models and compare with several alternative models. Next, I use the best performing model to evaluate the impact of training sample size on forecasting accuracy. Finally, after selecting the best model and the best sample size, I design two trading strategies using four different stock selection methods and compare the returns with two benchmarks: S&P 500 index and buy and hold strategy. Results show that, using the best sample size and Nelson-Siegel factors state space model which performs best in forecasting, the trading signals generated by yield curve prediction can be used as strategies to achieve higher average returns but lower Sharpe ratio than their benchmarks

Human Body Digital Twin: A Master Plan

The human body DT has the potential to revolutionize healthcare and wellness, but its responsible and effective implementation requires consideration of various factors. This article presents a comprehensive overview of the current status and future prospects of the human body DT and proposes a five-level roadmap for its development. The roadmap covers the development of various components, such as wearable devices, data collection, data analysis, and decision-making systems. The article also highlights the necessary support, security, cost, and ethical considerations that must be addressed in order to ensure responsible and effective implementation of the human body DT. The proposed roadmap provides a framework for guiding future development and offers a unique perspective on the future of the human body DT, facilitating new interdisciplinary research and innovative solutions in this rapidly evolving field.Comment: 3 figure

Using multitask classification methods to investigate the kinase-specific phosphorylation sites

<p>Abstract</p> <p>Background</p> <p>Identification of phosphorylation sites by computational methods is becoming increasingly important because it reduces labor-intensive and costly experiments and can improve our understanding of the common properties and underlying mechanisms of protein phosphorylation.</p> <p>Methods</p> <p>A multitask learning framework for learning four kinase families simultaneously, instead of studying each kinase family of phosphorylation sites separately, is presented in the study. The framework includes two multitask classification methods: the Multi-Task Least Squares Support Vector Machines (MTLS-SVMs) and the Multi-Task Feature Selection (MT-Feat3).</p> <p>Results</p> <p>Using the multitask learning framework, we successfully identify 18 common features shared by four kinase families of phosphorylation sites. The reliability of selected features is demonstrated by the consistent performance in two multi-task learning methods.</p> <p>Conclusions</p> <p>The selected features can be used to build efficient multitask classifiers with good performance, suggesting they are important to protein phosphorylation across 4 kinase families.</p