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 tweets’ meaning and calculate sentiment using the NLP transformers. We combine the daily historical Bitcoin price data with the daily sentiment and predict the next day’s 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: “Ampicillin” was tweeted 125,295 times, “Death” was tweeted 121,156 times, “Suffer” was tweeted 125,113 times, and “Vaccine” 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 “quality of life” 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 individuals’ 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 individuals’ lives. These findings shed light on the significant burden posed by Long COVID and its cascading effects on various aspects of individuals’ 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

Probability of Large Movements in Financial Markets

Based on empirical financial time-series, we show that the "silence-breaking" probability follows a super-universal power law: the probability of observing a large movement is inversely proportional to the length of the on-going low-variability period. Such a scaling law has been previously predicted theoretically [R. Kitt, J. Kalda, Physica A 353 (2005) 480], assuming that the length-distribution of the low-variability periods follows a multiscaling power law.Comment: 8 pages, 5 figure