Purpose – The purpose of this paper is to introduce the elementary concepts and fundamental principles of grey systems and the main components of grey systems theory. Also to discuss the astonishing progress that grey systems theory has made in the world of learning and its wide‐ranging applications in the entire spectrum of science.Design/methodology/approach – The characteristics of unascertained systems including incomplete information and inaccuracies in data are analysed and four uncertain theories: probability statistics, fuzzy mathematics, grey system and rough set theory are compared. The scientific principle of simplicity and how precise models suffer from inaccuracies are also shown.Findings – The four uncertain theories, probability statistics, fuzzy mathematics, grey system and rough set theory are examined with different research objects, different basic sets, different methods and procedures, different data requirements, different emphasis, different objectives and different characteristics....

A brief introduction to grey systems theory

As the COVID-19 pandemic continues its march around the world, an unprecedented amount of open data is being generated for genetics and epidemiological research. The unparalleled rate at which many research groups around the world are releasing data and publications on the ongoing pandemic is allowing other scientists to learn from local experiences and data generated in the front lines of the COVID-19 pandemic. However, there is a need to integrate additional data sources that map and measure the role of social dynamics of such a unique world-wide event into biomedical, biological, and epidemiological analyses. For this purpose, we present a large-scale curated dataset of over 152 million tweets, growing daily, related to COVID-19 chatter generated from January 1st to April 4th at the time of writing. This open dataset will allow researchers to conduct a number of research projects relating to the emotional and mental responses to social distancing measures, the identification of sources of misinformation, and the stratified measurement of sentiment towards the pandemic in near real time.

A large-scale COVID-19 Twitter chatter dataset for open scientific research -- an international collaboration

As the COVID-19 pandemic continues to spread worldwide, an unprecedented amount of open data is being generated for medical, genetics, and epidemiological research. The unparalleled rate at which many research groups around the world are releasing data and publications on the ongoing pandemic is allowing other scientists to learn from local experiences and data generated on the front lines of the COVID-19 pandemic. However, there is a need to integrate additional data sources that map and measure the role of social dynamics of such a unique worldwide event in biomedical, biological, and epidemiological analyses. For this purpose, we present a large-scale curated dataset of over 1.12 billion tweets, growing daily, related to COVID-19 chatter generated from 1 January 2020 to 27 June 2021 at the time of writing. This data source provides a freely available additional data source for researchers worldwide to conduct a wide and diverse number of research projects, such as epidemiological analyses, emotional and mental responses to social distancing measures, the identification of sources of misinformation, stratified measurement of sentiment towards the pandemic in near real time, among many others.

https://www.mdpi.com/2673-3986/2/3/24/pdf

The new transformed read-write Web has resulted in a rapid growth of user generated content on the Web resulting into a huge volume of unstructured data. A substantial part of this data is unstructured text such as reviews and blogs. Opinion mining and sentiment analysis (OMSA) as a research discipline has emerged during last 15 years and provides a methodology to computationally process the unstructured data mainly to extract opinions and identify their sentiments. The relatively new but fast growing research discipline has changed a lot during these years. This paper presents a scientometric analysis of research work done on OMSA during 2000–2016. For the scientometric mapping, research publications indexed in Web of Science (WoS) database are used as input data. The publication data is analyzed computationally to identify year-wise publication pattern, rate of growth of publications, types of authorship of papers on OMSA, collaboration patterns in publications on OMSA, most productive countries, institutions, journals and authors, citation patterns and an year-wise citation reference network, and theme density plots and keyword bursts in OMSA publications during the period. A somewhat detailed manual analysis of the data is also performed to identify popular approaches (machine learning and lexicon-based) used in these publications, levels (document, sentence or aspect-level) of sentiment analysis work done and major application areas of OMSA. The paper presents a detailed analytical mapping of OMSA research work and charts the progress of discipline on various useful parameters.

Analytical mapping of opinion mining and sentiment analysis research during 2000–2015

Risk management is an important field of construction industry and has gained more importance internationally due to the latest researches carried out on a large scale. However, this relatively new field requires more attention to bring some benefit. Construction projects are facing a number of risks which have negative effects on project objects such as time, cost and quality. This study is based on findings of a questionnaire-based survey on risk management in construction projects in Pakistan, reporting the significance of different type of risk, ultimate responsibility for them and the effectiveness of some most common risk management techniques practiced in the industry. Two types of risk management techniques were considered: preventive techniques which can be used before the start of a project to manage risks that are anticipated during the project execution; and remedial techniques that are used during the execution phase once a risk has already occurred. The study revealed that financial ...

/pdf/risk-management-in-construction-projects-3y0f40k1m1.pdf

Risk management in construction projects

The coronavirus outbreak has brought unprecedented measures, which forced the authorities to make decisions related to the instauration of lockdowns in the areas most hit by the pandemic. Social media has been an important support for people while passing through this difficult period. On November 9, 2020, when the first vaccine with more than 90% effective rate has been announced, the social media has reacted and people worldwide have started to express their feelings related to the vaccination, which was no longer a hypothesis but closer, each day, to become a reality. The present paper aims to analyze the dynamics of the opinions regarding COVID-19 vaccination by considering the one-month period following the first vaccine announcement, until the first vaccination took place in UK, in which the civil society has manifested a higher interest regarding the vaccination process. Classical machine learning and deep learning algorithms have been compared to select the best performing classifier. 2 349 659 tweets have been collected, analyzed, and put in connection with the events reported by the media. Based on the analysis, it can be observed that most of the tweets have a neutral stance, while the number of in favor tweets overpasses the number of against tweets. As for the news, it has been observed that the occurrence of tweets follows the trend of the events. Even more, the proposed approach can be used for a longer monitoring campaign that can help the governments to create appropriate means of communication and to evaluate them in order to provide clear and adequate information to the general public, which could increase the public trust in a vaccination campaign.

/pdf/the-longest-month-analyzing-covid-19-vaccination-opinions-22ul4ibugm.pdf

The Longest Month: Analyzing COVID-19 Vaccination Opinions Dynamics From Tweets in the Month Following the First Vaccine Announcement

Purpose – The purpose of this paper is to realise a complete analysis at the company level using grey systems theory for shaping the relations among variables.Design/methodology/approach – Starting from the symptoms that can be identified and moving forward to causes that determine a specific stage in a company's development and long‐term survival, all the aspects that can appear and affect a company's performance and bankruptcy are taken into account. Also, due to the fact that all the activities that took place in a company are running in an uncertain and a continually changing environment, grey systems theory was chosen to better shape the relations among the implied variables. Even when referring to a company's diagnosis or to its prediction, the involved aspects are presented and depicted. A numeric example is also computed in the last part of the paper.Findings – The results are convincing: not only that the diagnosis of the company can show the main elements, factors that are affecting a company's ...

Complete analysis of bankruptcy syndrome using grey systems theory

The novel coronavirus (SARS-CoV-2) has imposed the need for a series of social distancing restrictions worldwide to mitigate the scourge of the COVID-19 pandemic This applies to many domains, including airplane boarding and seat assignments As airlines are considering their passengers’ safety during the pandemic, boarding methods should be evaluated both in terms of social distancing norms and the resulting efficiency for the airlines The present paper analyzes the impact of a series of restrictions that have been imposed or mooted worldwide on the boarding methods used by the airlines, featuring the use of jet-bridges and one-door boarding To compare the efficacy of classical airplane boarding methods with respect to new social distancing norms, five metrics were used to evaluate their performance One metric is the time to complete the boarding of the airplane The other four metrics concern passenger health and reflect the potential exposure to the virus from other passengers through the air and surfaces (eg, headrests and luggage) touched by passengers We use the simulation platform in NetLogo to test six common boarding methods under various conditions The back-to-front by row boarding method results in the longest time to complete boarding but has the advantage of providing the lowest health risk for two metrics Those two metrics are based on passengers potentially infecting those passengers previously seated in the rows they traverse Interestingly, those two risks are reduced for most boarding methods when the social distance between adjacent passengers advancing down the aisle is increased, thus indicating an unanticipated benefit stemming from this form of social distancing The modified reverse pyramid by half zone method provides the shortest time to the completing boarding of the airplane and—along with the WilMA boarding method—provides the lowest health risk stemming from potential infection resulting from seat interferences Airlines have the difficult task of making tradeoffs between economic productivity and the resulting impact on various health risks

Camelia Delcea

Papers

The Longest Month: Analyzing COVID-19 Vaccination Opinions Dynamics From Tweets in the Month Following the First Vaccine Announcement

Complete analysis of bankruptcy syndrome using grey systems theory

Evaluating classical airplane boarding methods considering COVID-19 flying restrictions

Airplane Boarding Methods that Reduce Risk from COVID-19.

Social distancing in airplane seat assignments