Distant Reader - journal-chaosSolitonsFractals-cord

Introduction

This is a Distant Reader "study carrel", a set of structured data intended to help the student, researcher, or scholar use & understand a corpus.

This study carrel was created on 2021-05-30 by Eric Morgan <emorgan@nd.edu>. The carrel was created using the Distant Reader cord process, and the input was the result of a query applied to a local mirror of CORD, a data set of scholarly articles on the topic of COVID-19. The actual query was: facet_journal:"Chaos Solitons Fractals". The results of this query were saved in a cache and transformed into a set of plain text files. All of the analysis -- "reading" -- has been done against these plain text files. For example, a short narrative report has been created. This Web page is a more verbose version of that report.

All study carrels are self-contained -- no Internet connection is necessary to use them. Download this carrel for offline reading. The carrel is made up of many subdirectories and data files. The manifest describes each one in greater detail.

Size

There are 58 item(s) in this carrel, and this carrel is 243,977 words long. Each item in your study carrel is, on average, 4,206 words long. If you dig deeper, then you might want to save yourself some time by reading a shorter item. On the other hand, if your desire is for more detail, then you might consider reading a longer item. The following charts illustrate the overall size of the carrel.

Readability

On a scale from 0 to 100, where 0 is very difficult and 100 is very easy, the documents have an average readability score of 54. Consequently, if you want to read something more simplistic, then consider a document with a higher score. If you want something more specialized, then consider something with a lower score. The following charts illustrate the overall readability of the carrel.

Word Frequencies

By merely counting & tabulating the frequency of individual words or phrases, you can begin to get an understanding of the carrel's "aboutness". Excluding "stop words", some of the more frequent words include:

model, covid, time, infected, disease, i, epidemic, number, data, cases, fractional, rate, individuals, population, control, dynamics, system, coronavirus, infection, spread, using, models, analysis, based, will, order, transmission, pandemic, also, new, virus, infectious, parameters, study, different, case, mathematical, outbreak, given, one, susceptible, used, countries, china, people, social, may, following, results, novel

Using the three most frequent words, the three files containing all of those words the most are A Survey on Artificial Intelligence Approaches in Supporting Frontline Workers and Decision Makers for COVID-19 Pandemic, Social distancing versus early detection and contacts tracing in epidemic management, and Applicability of time fractional derivative models for simulating the dynamics and mitigation scenarios of COVID-19.

The most frequent two-word phrases (bigrams) include:

novel coronavirus, differential equations, reproduction number, sir model, infected individuals, optimal control, fractional order, infectious diseases, fractional derivative, social distancing, basic reproduction, confirmed cases, mathematical model, coronavirus disease, epidemic model, time series, transmission dynamics, public health, infected population, free equilibrium, epidemic spreading, new cases, infected people, asymptotically stable, endemic equilibrium, proposed model, total number, machine learning, infected cases, deep learning, stock markets, infectious disease, fractional differential, chaos solitons, mathematical models, solitons fractals, doc id, fractals doi, cord uid, disease transmission, control measures, susceptible individuals, model parameters, total population, seir model, initial conditions, susceptible population, authors declare, contact tracing, control strategies

And the three file that use all of the three most frequent phrases are Solutions of a disease model with fractional white noise Forecasting of COVID-19 pandemic: From integer derivatives to fractional derivatives, and Implicit Riesz wavelets based-method for solving singular fractional integro-differential equations with applications to hematopoietic stem cell modeling.

While often deemed superficial or sophomoric, rudimentary frequencies and their associated "word clouds" can be quite insightful:

Keywords

Sets of keywords -- statistically significant words -- can be enumerated by comparing the relative frequency of words with the number of times the words appear in an entire corpus. Some of the most statistically significant keywords in the carrel include:

covid-19, model, fractional, sars, india, epidemic, table, sir, lstm, italy, control, china, case, caputo, arima, vaccine, vaccination, usa, topic, são, system, spread, sirs, sidarthe, room, risk, riesz, rate, quarantine, player, paulo, pakistan, optimal, n−1, new, mutation, morocco, market, logistic, lle, legendre, learning, lda, january, individual, hiv, heilongjiang, february, disease, deep

And now word clouds really begin to shine:

Topic Modeling

Topic modeling is another popular approach to connoting the aboutness of a corpus. If the study carrel could be summed up in a single word, then that word might be model, and Global dynamics of a fractional order model for the transmission of HIV epidemic with optimal control is most about that word.

If the study carrel could be summed up in three words ("topics") then those words and their significantly associated titles include:

If the study carrel could be summed up in five topics, and each topic were each denoted with three words, then those topics and their most significantly associated files would be:

covid, model, data - A Survey on Artificial Intelligence Approaches in Supporting Frontline Workers and Decision Makers for COVID-19 Pandemic
model, covid, epidemic - Social distancing versus early detection and contacts tracing in epidemic management
fractional, model, control - Global dynamics of a fractional order model for the transmission of HIV epidemic with optimal control
covid, model, time - Predictions for COVID-19 with Deep Learning Models of LSTM, GRU and Bi-LSTM
topic, topics, media - Analysis of spatiotemporal characteristics of big data on social media sentiment with COVID-19 epidemic topics

Moreover, the totality of the study carrel's aboutness, can be visualized with the following pie chart:

Noun & Verbs

Through an analysis of your study carrel's parts-of-speech, you are able to answer question beyonds aboutness. For example, a list of the most frequent nouns helps you answer what questions; "What is discussed in this collection?":

model, time, disease, number, epidemic, cases, data, rate, individuals, population, control, system, dynamics, infection, models, order, parameters, transmission, virus, coronavirus, analysis, pandemic, spread, case, outbreak, countries, people, study, results, function, equations, equilibrium, γ, days, measures, value, parameter, equation, period, quarantine, section, contact, lockdown, values, paper, health, class, β, infections, state

An enumeration of the verbs helps you learn what actions take place in a text or what the things in the text do. Very frequently, the most common lemmatized verbs are "be", "have", and "do"; the more interesting verbs usually occur further down the list of frequencies:

using, show, give, based, following, infected, considered, seen, proposed, spreading, obtain, applied, take, reported, increased, predict, confirmed, described, find, reduce, modeled, presented, estimate, assume, recovered, representing, becomes, develops, provide, known, studied, controlling, includes, observe, makes, defined, compared, exposed, get, changed, decrease, indicate, affected, leading, analyze, solve, means, detected, identify, introducing

Proper Nouns

An extraction of proper nouns helps you determine the names of people and places in your study carrel.

COVID-19, Fig, China, SIR, S, SARS, India, Wuhan, CoV-2, Table, Coronavirus, March, Italy, Caputo, Eq, R, April, D, −, HIV, USA, covid-19, Health, T, E, β, SEIR, Analysis, ARIMA, January, Chaos, M, LSTM, COVID, Figure, Solitons, May, N, sha, n−1, DOI, Lyapunov, A, Theorem, I(t, Covid-19, Q, Model, June, ApEn

An analysis of personal pronouns enables you to answer at least two questions: 1) "What, if any, is the overall gender of my study carrel?", and 2) "To what degree are the texts in my study carrel self-centered versus inclusive?"

we, it, i, our, its, their, they, us, them, one, he, his, themselves, itself, her, she, your, you, him, 's, u, s, ourselves, m, , β, ŝ, theirs, oneself, herewith, 328

Below are words cloud of your study carrel's proper & personal pronouns.

Adjectives & Verbs

Learning about a corpus's adjectives and adverbs helps you answer how questions: "How are things described and how are things done?" An analysis of adjectives and adverbs also points to a corpus's overall sentiment. "In general, is my study carrel positive or negative?"

fractional, infected, different, new, infectious, covid-19, susceptible, mathematical, social, asymptomatic, novel, real, numerical, total, positive, first, non, optimal, initial, differential, basic, many, stochastic, public, global, early, possible, various, high, stable, effective, free, important, endemic, daily, current, second, human, epidemiological, large, available, symptomatic, derivative, average, cumulative, local, constant, similar, potential, negative

also, therefore, however, well, respectively, hence, now, asymptotically, furthermore, even, finally, still, significantly, first, locally, recently, namely, always, moreover, highly, globally, much, almost, easily, consequently, far, currently, clearly, already, widely, initially, indeed, often, similarly, just, numerically, exponentially, yet, together, rapidly, meanwhile, fully, especially, effectively, worldwide, greatly, newly, directly, completely, previously

Next steps

There is much more to a study carrel than the things outlined above. Use this page's menubar to navigate and explore in more detail. There you will find additional features & functions including: ngrams, parts-of-speech, grammars, named entities, topic modeling, a simple search interface, etc.

Again, study carrels are self-contained. Download this carrel for offline viewing and use.

Thank you for using the Distant Reader.