How Does Travel Satisfaction Affect Preference for Shared Electric Vehicles? An Empirical Study Using Large - Scale Monitoring Data and Online Text Mining

2021 ◽  
Author(s):  
Bin Zhang ◽  
Yi Yi ◽  
Pengyu Zou ◽  
Zhaohua Wang
Keyword(s):  
Text Mining ◽  
Empirical Study ◽  
Electric Vehicles ◽  
Large Scale ◽  
Monitoring Data ◽  
Travel Satisfaction

Investigating Diseases and Chemicals in COVID-19 Literature with Text Mining (Preprint)

2020 ◽  
Author(s):  
Amir Karami ◽  
Brandon Bookstaver ◽  
Melissa Nolan
Keyword(s):  
Text Mining ◽  
Literature Review ◽  
Topic Modeling ◽  
Large Scale ◽  
Clinical Manifestations ◽  
International Health ◽  
Research Papers ◽  
Strategic Plans ◽  
Funding Agencies ◽  
The Relationship

BACKGROUND The COVID-19 pandemic has impacted nearly all aspects of life and has posed significant threats to international health and the economy. Given the rapidly unfolding nature of the current pandemic, there is an urgent need to streamline literature synthesis of the growing scientific research to elucidate targeted solutions. While traditional systematic literature review studies provide valuable insights, these studies have restrictions, including analyzing a limited number of papers, having various biases, being time-consuming and labor-intensive, focusing on a few topics, incapable of trend analysis, and lack of data-driven tools. OBJECTIVE This study fills the mentioned restrictions in the literature and practice by analyzing two biomedical concepts, clinical manifestations of disease and therapeutic chemical compounds, with text mining methods in a corpus containing COVID-19 research papers and find associations between the two biomedical concepts. METHODS This research has collected papers representing COVID-19 pre-prints and peer-reviewed research published in 2020. We used frequency analysis to find highly frequent manifestations and therapeutic chemicals, representing the importance of the two biomedical concepts. This study also applied topic modeling to find the relationship between the two biomedical concepts. RESULTS We analyzed 9,298 research papers published through May 5, 2020 and found 3,645 disease-related and 2,434 chemical-related articles. The most frequent clinical manifestations of disease terminology included COVID-19, SARS, cancer, pneumonia, fever, and cough. The most frequent chemical-related terminology included Lopinavir, Ritonavir, Oxygen, Chloroquine, Remdesivir, and water. Topic modeling provided 25 categories showing relationships between our two overarching categories. These categories represent statistically significant associations between multiple aspects of each category, some connections of which were novel and not previously identified by the scientific community. CONCLUSIONS Appreciation of this context is vital due to the lack of a systematic large-scale literature review survey and the importance of fast literature review during the current COVID-19 pandemic for developing treatments. This study is beneficial to researchers for obtaining a macro-level picture of literature, to educators for knowing the scope of literature, to journals for exploring most discussed disease symptoms and pharmaceutical targets, and to policymakers and funding agencies for creating scientific strategic plans regarding COVID-19.

scholarly journals Characterization of the Compressive Load on a Lithium-Ion Battery for Electric Vehicle Application

Machines ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 71
Author(s):  
Seyed Saeed Madani ◽  
Erik Schaltz ◽  
Søren Knudsen Kær
Keyword(s):  
Electrochemical Impedance Spectroscopy ◽  
Impedance Spectroscopy ◽  
Lithium Ion Batteries ◽  
Lithium Ion Battery ◽  
Electric Vehicles ◽  
Large Scale ◽  
Electrochemical Impedance ◽  
Applied Pressure ◽  
Lithium Ion ◽  
Battery Cells

Lithium-ion batteries are being implemented in different large-scale applications, including aerospace and electric vehicles. For these utilizations, it is essential to improve battery cells with a great life cycle because a battery substitute is costly. For their implementation in real applications, lithium-ion battery cells undergo extension during the course of discharging and charging. To avoid disconnection among battery pack ingredients and deformity during cycling, compacting force is exerted to battery packs in electric vehicles. This research used a mechanical design feature that can address these issues. This investigation exhibits a comprehensive description of the experimental setup that can be used for battery testing under pressure to consider lithium-ion batteries’ safety, which could be employed in electrified transportation. Besides, this investigation strives to demonstrate how exterior force affects a lithium-ion battery cell’s performance and behavior corresponding to static exterior force by monitoring the applied pressure at the dissimilar state of charge. Electrochemical impedance spectroscopy was used as the primary technique for this research. It was concluded that the profiles of the achieved spectrums from the experiments seem entirely dissimilar in comparison with the cases without external pressure. By employing electrochemical impedance spectroscopy, it was noticed that the pure ohmic resistance, which is related to ion transport resistance of the separator, could substantially result in the corresponding resistance increase.

scholarly journals Investigation of the Impact of Large-Scale Integration of Electric Vehicles for a Swedish Distribution Network

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4717 ◽  
Author(s):  
Sylvester Johansson ◽  
Jonas Persson ◽  
Stavros Lazarou ◽  
Andreas Theocharis
Keyword(s):  
Electric Vehicles ◽  
Power Distribution ◽  
Large Scale ◽  
Electrical Power ◽  
Distribution Network ◽  
Transport Sector ◽  
Smart Charging ◽  
Large Scale Integration ◽  
Scale Integration ◽  
The Impact

Social considerations for a sustainable future lead to market demands for electromobility. Hence, electrical power distribution operators are concerned about the real ongoing problem of the electrification of the transport sector. In this regard, the paper aims to investigate the large-scale integration of electric vehicles in a Swedish distribution network. To this end, the integration pattern is taken into consideration as appears in the literature for other countries and applies to the Swedish culture. Moreover, different charging power levels including smart charging techniques are examined for several percentages of electric vehicles penetration. Industrial simulation tools proven for their accuracy are used for the study. The results indicate that the grid can manage about 50% electric vehicles penetration at its current capacity. This percentage decreases when higher charging power levels apply, while the transformers appear overloaded in many cases. The investigation of alternatives to increase the grid’s capabilities reveal that smart techniques are comparable to the conventional re-dimension of the grid. At present, the increased integration of electric vehicles is manageable by implementing a combination of smart gird and upgrade investments in comparison to technically expensive alternatives based on grid digitalization and algorithms that need to be further confirmed for their reliability for power sharing and energy management.

A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Feng Shi ◽  
Liuqing Chen ◽  
Ji Han ◽  
Peter Childs
Keyword(s):  
Information Retrieval ◽  
Network Analysis ◽  
Text Mining ◽  
Engineering Design ◽  
Large Scale ◽  
Semantic Network ◽  
Document Retrieval ◽  
Design Information ◽  
Improve Design ◽  
Correlation Degree

With the advent of the big-data era, massive information stored in electronic and digital forms on the internet become valuable resources for knowledge discovery in engineering design. Traditional document retrieval method based on document indexing focuses on retrieving individual documents related to the query, but is incapable of discovering the various associations between individual knowledge concepts. Ontology-based technologies, which can extract the inherent relationships between concepts by using advanced text mining tools, can be applied to improve design information retrieval in the large-scale unstructured textual data environment. However, few of the public available ontology database stands on a design and engineering perspective to establish the relations between knowledge concepts. This paper develops a “WordNet” focusing on design and engineering associations by integrating the text mining approaches to construct an unsupervised learning ontology network. Subsequent probability and velocity network analysis are applied with different statistical behaviors to evaluate the correlation degree between concepts for design information retrieval. The validation results show that the probability and velocity analysis on our constructed ontology network can help recognize the high related complex design and engineering associations between elements. Finally, an engineering design case study demonstrates the use of our constructed semantic network in real-world project for design relations retrieval.

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