13 research outputs found
Impact of euro adoption in emerging European countries
We study the impact of the euro on emerging European countries by investigating three country groups: (1) seventeen Eurozone countries, (2) seven eu Eastern and Central European (ECE) members using local currencies, and (3) six EU candidates. We analyze macroeconomic indicators and propose models to investigate whether similar or different indicators influence sovereign debt for each group. We find that exports and unemployment are positively related to sovereign debt while market capitalization shows negative relation with sovereign debt. We argue that the recent European sovereign debt crisis has raised serious challenges for the Eurozone, and propose that EU ECE members and EU candidates delay the adoption of the euro
Challenges and opportunities in ESG investments
Accepted manuscrip
New measures of journal impact based on the number of citations and PageRank
The number of citations has been used for measuring the significance of a paper. Moreover, we have the following question: which paper is the most important if there are some papers with the same number of citations? Some measures have been introduced to answer this question: one of them is PageRank. We use the Science Citation Index Expanded from 1981 to 2015 to calculate the number of citations and the Google number in the citation network consisting of 34,666,719 papers and 591,321,826 citations. We clarify the positive linear relationship between the number of citations and the Google number, as well as extract some outliers from this positive linear relationship. These outliers are considered to be extremely prestigious papers. Furthermore, we calculate the mean values of the number of citations and the Google number for all journals, construct a new measure of journal influence, and extract extremely prestigious journals. This new measure has a positive and medium correlation with the impact factor, Eigenfactor score, and SCImago Journal Rank.Published versio
Examining mental illness trends in the United States from 2006 to 2019
We investigate the characteristics of medical expenditures associated with mental illness hospitalizations using the Truven Health MarketScan Database. We focus on the inpatient admissions due to mental illness of adults aged 1S to 64 between 2006 to 2019. We aim to answer the following questions: (1) Did the financial crisis of 2008 impact mental health in the U.S.?(2) What are the other macro-level (socioeconomic and regulartory) and micro-level (individualpatient related) factors that affect the cost of inpatient care due to mental illness; (3) Did mental illness affect men and women differently? (4) How were different regions within the U.S. affected by mental illness?Accepted manuscrip
Bitcoin price prediction using transfer learning on financial micro-blogs
We present a methodology for predicting the price of
Bitcoin using Twitter data and historical Bitcoin prices. Bitcoin is
the largest cryptocurrency that, in terms of market capitalization,
represents over 110 billion dollars. The news volume is rapidly
growing, and Twitter is increasingly used as a news source
influencing purchase decisions by informing users of the currency
and its popularity. Using modern Natural Language Processing
models for transfer learning, we analyze tweetsâ meaning and
calculate sentiment using the NLP transformers. We combine
the daily historical Bitcoin price data with the daily sentiment
and predict the next dayâs price using auto-regressive models for
time-series forecasting.
The results show that modern approaches for sentiment
analysis, time-series forecasting, and transfer-learning are applicable for predicting Bitcoin price when we include sentiment
extracted from financial micro-blogs as input. The results show
improvement when compared to the old approaches using only
historical price data. Additionally, we show that the NLP models
based on transfer-learning methodologies improve the efficiency
in sentiment extraction in financial micro-blogs compared to
standard sentiment extraction methods.Published versio
Analysis of Long COVID Phenotypes and their Impact on Mental Health and Daily Functioning: Insights from Twitter
In this study, we conducted an investigation into Long COVID from a user perspective, utilizing
Twitter social media data. Prior to analysis, the data underwent preprocessing to obtain raw text
per tweet. Our analysis commenced with basic statistical analysis and subsequently expanded to
identify characteristic periods for the phenotypes based on dynamic timelines. We also explored the
relationships between the phenotypes, as well as the interdependence between phenotypes and
geolocation.
In the context of this research, an analysis was conducted on a collection of tweets that encompassed
the timeframe from March 2020 to March 2022. The dataset consisted of approximately 1.9
million tweets. In order to concentrate on word phrases, extraneous elements such as mentions,
emoticons, links, and hashtags were eliminated. Subsequently, a process of lemmatization was
performed. For the purpose of reducing the number of distinct phenotypes under investigation
and facilitating the presentation of results, the collected data was categorized into five overarching
groups: Cardiovascular, Respiratory, Daily Living, Neurological and Mental Health, and Other.
The statistical data regarding the most commonly used words by individuals describing their
experiences during the Long COVID period are as follows: âAmpicillinâ was tweeted 125,295 times,
âDeathâ was tweeted 121,156 times, âSufferâ was tweeted 125,113 times, and âVaccineâ was
tweeted 108,968 times. We observe distinct patterns in the emergence of certain phenotypes
during this period, particularly in relation to the quality of life. On August 1, 2020, the term âquality
of lifeâ was mentioned in only 223 tweets, whereas one year later, during the same month, this
phenotype garnered 1,663 tweets.
Our findings reveal that the occurrence of Long COVID phenotypes is influenced by both temporal and
geographical factors. The analysis shows a clear and notable trend within the dataset. Specifically,
it is observed that neurological symptoms, along with symptoms that impede individualsâ daily
functioning, exhibit the highest prevalence, particularly during the latter half of the analyzed tweet
period. This period corresponds to a time when an increasing number of individuals have recovered
from COVID-19 and are reporting their experiences with Long COVID. Notably, fatigue, depression,
stress, and anxiety emerge as the most prevalent phenotypes.
This scientific investigation of the complex interactions between Long COVID phenotypes, mental
health, and the manifestation of diverse symptoms is offering insights into the profound consequences
on individualsâ lives. These findings shed light on the significant burden posed by Long COVID and its
cascading effects on various aspects of individualsâ well-being and society at large.Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 202
Size effects on the quenching to the normal state in YBa2Cu3O7-delta thin film superconductors
To probe the quenching mechanisms under high current densities,
current-voltage curves have been measured in YBa2Cu3O7-delta thin film
microbridges with widths lower than the thermal diffusion length. This
condition was obtained by using microbridge widths under 100 micrometers and
stepped ramps of one millisecond step duration. Whereas the flux-flow
resistivity is found to be microbridge-width independent, strong width
dependence of the quenching current density is observed. These results provide
a direct experimental demonstration that for high current densities varying in
the millisecond range the transition to a highly dissipative state is due to
self heating driven by "conventional" (non-singular) flux flow effects.Comment: RevTex4, 5 pages, including 4 eps figures. To appear in Physical
Review
Predicting companies stock price direction by using sentiment analysis of news articles
This paper summarizes our experience teaching
several courses at Metropolitan College of Boston University Computer Science department over five years. A number of innovative teaching techniques are presented in this paper. We
specifically address the role of a project archive, when designing a course. This research paper explores survey results from every running of courses, from 2014 to 2019. During each class, students participated in two distinct surveys: first, dealing with key learning outcomes, and, second, with teaching techniques used. This paper makes several practical recommendations based on the analysis of collected data. The research validates the value of a sound repository of technical term projects and the role such repository plays in effective teaching and learning of computer science courses.Published versio
Exploring Changes in Diagnoses during the COVID-19 Era: Comparative Analysis
The healthcare sector is just one of several areas of society that have been significantly impacted
by the COVID-19 pandemic. This paper aims to analyze the changes observed in the medical
professionâs approach to diagnosing diseases between the pre-pandemic year of 2019 and the
pandemic year of 2020. By examining these shifts, we explore how medical professionals have
adapted their treatment strategies, leading to modifications in diagnosis for various diseases.
Based on our visualization, shown in Figure 1, we observed that the diagnoses of Obstructive
Sleep Apnea and End stage renal disease had consistent distributions in both 2019 and 2020.
Also we need to mention, the count value for Obstructive Sleep Apnea was higher in 2020,
whereas in 2019, the count value was higher for End stage renal disease, showing their
representation in each year. We can conclude that the pandemic has resulted in a marked
increase in the occurrence of specific diagnoses compared to the previous year, some of them
being acute pharyngitis-sore throat (J029), gastro-oesophageal reflux disease (K219) and pure
hypercholesterolemia - unspecified (E7800), as can be seen on Figure 1.
A notable variation can be observed when examining the months of November and December
in 2020. In these months, the diagnosis Contact with and (suspected) exposure to other viral
communicable diseases transitions from the third to the second position, indicating a higher
occurrence of COVID-19 in December compared to November. This shift in ranking provides
valuable insights into the increased prevalence of this diagnosis during the month of December.
Through this analysis, we aim to examine the transformations that have taken place as
a result of the pandemic, particularly in terms of the diagnosis of a specific disease, which
has undergone notable changes compared to the pre-pandemic period. We highlight several
significant changes that have occurred in defining diagnoses, showcasing the variations
observed over the course of a year.Book of abstract: 4th Belgrade Bioinformatics Conference, June 19-23, 202