Improved HPC method for nonlinear wave tank

The recently developed Harmonic Polynomial Cell (HPC) method has been proved to be a promising choice for solving potential-flow Boundary Value Problem (BVP). In this paper, a flux method is proposed to consistently deal with the Neumann boundary condition of the original HPC method and enhance the accuracy. Moreover, fixed mesh algorithm with free surface immersed is developed to improve the computational efficiency. Finally, a two dimensional (2D) multi-block strategy coupling boundary-fitted mesh and fixed mesh is proposed. It limits the computational costs and preserves the accuracy. A fully nonlinear 2D numerical wave tank is developed using the improved HPC method as a verification. Keywords: Harmonic polynomial cell method, Potential-flow theory, Flux method, Fixed mesh, Multi-block strategy, Nonlinear numerical wave tan

Probabilistic Harmonic Calculation in Distribution Networks with Electric Vehicle Charging Stations

Integrating EV charging station into power grid will bring impacts on power system, among which the most significant one is the harmonic pollution on distribution networks. Due to the uncertainty of the EV charging process, the harmonic currents brought by EV charging stations have a random nature. This paper proposed a mathematical simulation method for studying the working status of charging stations, which considers influencing factors including random leaving factor, electricity price, and waiting time. Based on the proposed simulation method, the probability distribution of the harmonic currents of EV charging stations is obtained and used in the calculation of the probability harmonic power flow. Then the impacts of EVs and EV charging stations on distribution networks can be analyzed. In the case study, the proposed simulation and analysis method is implemented on the IEEE-34 distribution network. The influences of EV arrival rates, the penetration rate, and the accessing location of EV charging station are also investigated. Results show that this research has good potential in guiding the planning and construction of charging station

DEVELOPMENT AND APPLICATION OF SINGLE CELL MASS SPECTROMETRY TECHNIQUES FOR NON-ADHERENT CELL ANALYSIS

Mass spectrometry (MS) analysis of biological samples is traditionally carried out using extractions from large populations of cells, concealing the information from individual cells. In contrast, the drawbacks of traditional methods can be overcome by single cell MS (SCMS) methods, and this approach is particularly suitable to study rare types of cells that are hard to achieve or culture, including primary cells, stem cells, and patient-derived cells. Due to the super capability of MS technique, a series of SCMS methods have been rapidly developed to investigate undiscovered cellular mechanisms of a broad range of cells. My studies led to the development of two novel sampling and ionization devices for analyzing non-adherent single cells in ambient conditions: the redesigned T-probe and micropipette needle. The redesigned T-probe can be applied for real-time SCMS analysis of live single cells, without losing cell content during the analysis. In addition, this device allows for relatively long ion signal acquisition time for more molecular structure identification. The development and application of this device are described in Chapter 3. The micropipette needle is another technology for non-adherent single cell analysis. Particularly, this device can be used for reactive SCMS experiments, in which chemical reactions between cellular species and reagents can be performed prior to MS analysis, allowing for versatile experimental designs. In Chapter 4, the micropipette device was used to conduct both regular and reactive SCMS analysis of the same single cell to identify double bond locations of unsaturated lipid isomers, which are critical for the understanding of lipid biochemistry and therapeutic targets in diseases

Empirical analysis on factors influencing the choice of exchange rate regime

Since the Bretton Woods system collapsed in 1973, the exchange rate regimes mainly consisted of fixed and floating regimes. After entering the 21st century, the intermediate exchange regime has also been adopted by many countries. As a price gauge for international commerce, the exchange rate is crucial. The correlation between nominal exchange rate swings and exchange regime is strong. Since the rapid development of emerging countries and their important position in the international financial field, the exchange rate regime adopted by emerging countries has also been a concern by scholars. Adopting a suitable exchange rate regime will benefit national development, guaranteeing rapid economic growth and reducing the possibility of a monetary crisis. This dissertation holds that there have been several kinds of research on the selection of exchange regimes, but the main idea is unclear, and the definition of relevant concepts is vague. As a result, this article will define and sort related concepts, including the objective of this dissertation: an empirical analysis of factors influencing the selection of exchange regimes in emerging countries. The exchange rate regime selection of all emerging countries will be used as research samples for regression analysis. This paper analyzes the reasons behind the choice of exchange regimes and puts forward suggestions to benefit emerging countries' healthy economic development

EVKG: An Interlinked and Interoperable Electric Vehicle Knowledge Graph for Smart Transportation System

Over the past decade, the electric vehicle industry has experienced unprecedented growth and diversification, resulting in a complex ecosystem. To effectively manage this multifaceted field, we present an EV-centric knowledge graph (EVKG) as a comprehensive, cross-domain, extensible, and open geospatial knowledge management system. The EVKG encapsulates essential EV-related knowledge, including EV adoption, electric vehicle supply equipment, and electricity transmission network, to support decision-making related to EV technology development, infrastructure planning, and policy-making by providing timely and accurate information and analysis. To enrich and contextualize the EVKG, we integrate the developed EV-relevant ontology modules from existing well-known knowledge graphs and ontologies. This integration enables interoperability with other knowledge graphs in the Linked Data Open Cloud, enhancing the EVKG's value as a knowledge hub for EV decision-making. Using six competency questions, we demonstrate how the EVKG can be used to answer various types of EV-related questions, providing critical insights into the EV ecosystem. Our EVKG provides an efficient and effective approach for managing the complex and diverse EV industry. By consolidating critical EV-related knowledge into a single, easily accessible resource, the EVKG supports decision-makers in making informed choices about EV technology development, infrastructure planning, and policy-making. As a flexible and extensible platform, the EVKG is capable of accommodating a wide range of data sources, enabling it to evolve alongside the rapidly changing EV landscape

Effects of Additives on the Quality of \u3cem\u3eLeymus chinensis\u3c/em\u3e Silage

Leymus chinensis, which is a perennial plant with good palatability and high forage value, is widespread from the southern Chinese Loess plateau to northern Russian Baikal and from the Sanjiang plain of eastern China to Ulaanbaatar in Mongolia. Grazing and hay are the most common ways Leymus chinensis is utilized. The quality of Leymus chinensis silage is poorly understood. This study was conducted to investigate the fermentation quality and the in vitro digestibility of Leymus chinensis silage treated with lactic acid bacteria (LAB) and cellulose (CE)

Improving Question Generation with Sentence-level Semantic Matching and Answer Position Inferring

Taking an answer and its context as input, sequence-to-sequence models have made considerable progress on question generation. However, we observe that these approaches often generate wrong question words or keywords and copy answer-irrelevant words from the input. We believe that lacking global question semantics and exploiting answer position-awareness not well are the key root causes. In this paper, we propose a neural question generation model with two concrete modules: sentence-level semantic matching and answer position inferring. Further, we enhance the initial state of the decoder by leveraging the answer-aware gated fusion mechanism. Experimental results demonstrate that our model outperforms the state-of-the-art (SOTA) models on SQuAD and MARCO datasets. Owing to its generality, our work also improves the existing models significantly.Comment: Revised version of paper accepted to Thirty-fourth AAAI Conference on Artificial Intelligenc