308 research outputs found
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
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