Fractal correlations in the Covid-19 genome sequence via multivariate rescaled range analysis

An approach based on fractal scaling analysis to characterize the organization of the Covid-19 genome sequences is presented in this work. The method is based on a multivariate version of the fractal rescaled range analysis implemented on a sliding window scheme to detect variations of long-range correlations over the genome sequence domains. As a preliminary step, the nucleotide sequence is mapped in a numerical sequence by following a Voss rule, resulting in a multichannel sequence represented as a binary matrix. Fractal correlations, quantified in terms of the Hurst exponent, depending on the region of the sequence, where the Covid-19 genome sequences are predominantly random, with some patches of weak long-range correlations. The analysis shows that the regions of randomness are more abundant in the Covid-19 sequences than in the primitive SARS sequence, which suggests that the Covid-19 virus possesses a more diverse genomic structure for replication and infection. The analysis constrained to the surface glycoprotein region shows that the Covid-19 sequence is less random as compared to the SARS sequence, which indicates that the Covid-19 virus can undergo more ordered replications of the spike protein. The Omicron variation exhibits an interesting pattern with some randomness similarities with the other SARS and the Covid-19 genome sequences. Overall, the results show that the multivariate rescaled range analysis provides a suitable framework to assess long-term correlations hidden in the internal organization of the Covid-19 genome sequence. © 2023

Fractal correlations in the Covid-19 genome sequence via multivariate rescaled range analysis

An approach based on fractal scaling analysis to characterize the organization of the Covid-19 genome sequences is presented in this work. The method is based on a multivariate version of the fractal rescaled range analysis implemented on a sliding window scheme to detect variations of long-range correlations over the genome sequence domains. As a preliminary step, the nucleotide sequence is mapped in a numerical sequence by following a Voss rule, resulting in a multichannel sequence represented as a binary matrix. Fractal correlations, quantified in terms of the Hurst exponent, depending on the region of the sequence, where the Covid-19 genome sequences are predominantly random, with some patches of weak long-range correlations. The analysis shows that the regions of randomness are more abundant in the Covid-19 sequences than in the primitive SARS sequence, which suggests that the Covid-19 virus possesses a more diverse genomic structure for replication and infection. The analysis constrained to the surface glycoprotein region shows that the Covid-19 sequence is less random as compared to the SARS sequence, which indicates that the Covid-19 virus can undergo more ordered replications of the spike protein. The Omicron variation exhibits an interesting pattern with some randomness similarities with the other SARS and the Covid-19 genome sequences. Overall, the results show that the multivariate rescaled range analysis provides a suitable framework to assess long-term correlations hidden in the internal organization of the Covid-19 genome sequence.

The Double-slit experiment and Feynman Amplitudes to Interpret the Rise of Omicron at the Covid-19 Pandemic at 2022

As perceived at mostly countries, after the 3th or 4th wave of Covid-19 pandemic, its potential end might be coming. In contrast to previous waves, the last one exhibits different characteristics such as a highly exponential behavior or also a sharped Gauss behavior as seen at the huge number of infections at America and Europe. In this paper is proposed the idea that the whole pandemic is clearly composed by a sinusoid and Gaussian functions. This claim is based at the world-wide data that exhbits a similar shape with the proposed scheme at this study. Thus, the pandemic can also be seen as a signal-noise being the Omicron variant the signal whereas the previous mutations can be seen as their background events or noise. © 2022 IEEE.

THE IMPACT OF LÉVY JUMPS AND WHITE NOISE ON THE DYNAMICS OF A COVID-19 EPIDEMIC MODEL: A CASE STUDY OF THE KINGDOM OF SAUDI ARABIA

Recently, the pandemic of Covid-19 attacked many countries, and many public establishments were closed because of this pandemic. As well, the Covid-19 pandemic hurt the economy and various activities of countries around the world. Mathematical Modeling and numerical analysis can help governments to find solutions for controlling the propagation of the Covid-19 pandemic. In the present paper, we consider a stochastic Lévy jumps epidemic model that models the propagation of Covid-19 in a population divided into six groups of individuals. We investigate the extinction and persistence of our stochastic systems with and without Lévy jumps. Furthermore, we give a detailed numerical comparison of disease for the stochastic and deterministic systems. © 2022 the author(s).

Accounting the factor of randomity of social processes in prediction of demand for electric energy

Мета. Урахування фактору випадковосл сощальних процесш при прогнозуванш попиту на електричну енерпю для зменшення похибки. Методика. Апарат математично! статистики, методш лшшного програмування, теорп нечггких множин i методiв експертного оцшювання, теорй' шкал, Байесовський п1дх1д до моделей прогнозування, комп'ютерне моделювання. Результаты. Проаналiзована динамiка споживання електрично! енергп за рiзнi перiоди часу, встановлено вплив фактору пандемп на процес формування попиту на електричну енерпю. Розроблена вербально-числова шкала для комплексного оцшювання впливу на попит на електричну енерпю такого складного сощального явища, як пандемш. Сформована модель прогнозування попиту на електричну енерпю з використанням Байесовського подходу та ощнки експерта, що дозволила використати ретроспективш данi споживання електрично! енергп та врахувати невизначенiсть соцiального фактору впливу пандемп. Наукова новизна. Набула подальшого розвитку модель прогнозування попиту на електричну енерпю, яка, на вщмшу в1д iнших, ураховуе фактор випадковостi соцiальних процеив i вербально-числову шкалу, що дозволяе зменшити похибку прогнозування споживання електрично! енергп. Практична значимтсть. Результата дослщження кориснi для пщприемств, що спецiалiзуються на генерацй', передачi й розподшу електрично! енергп споживачам. Представленi результата надають можливють зменшити похибку прогнозування попиту на електричну енерпю при врахуванш фактору випадковосл сощальних процешв.Alternate :Purpose. Taking into account the factor of randomness of social processes when forecasting the demand for electric energy to reduce the error. Methodology. Apparatus of mathematical statistics, linear programming methods, fuzzy set theory and expert assessment methods, scale theory, Bayesian approach to forecasting models, computer modeling. Findings. The dynamics of consumption of electric energy for different periods of time is analyzed, the influence of the pandemic factor on the process of formation of demand for electric energy is established. A verbal-numerical scale has been developed for a comprehensive assessment of the impact on the demand for electric energy of such a complex social phenomenon as a pandemic. A model for forecasting the demand for electrical energy was formed using the Bayesian approach and an expert's assessment, which made it possible to use retrospective data on electrical energy consumption and take into account the uncertainty of the social factor influencing the pandemic. Originality. The model for forecasting the demand for electrical energy has been further developed, which, unlike others, takes into account the factor of randomness of social processes and a verbal-numerical scale, which makes it possible to reduce the error in predicting the consumption of electrical energy. Practic l value. The research results are useful for enterprises specializing in the generation, transmission and distribution of electrical energy to consumers. The presented results make it possible to reduce the error in forecasting the demand for electric energy, taking into account the factor of randomness of social processes.

Pandemic of Covid-19 as Global Entropy: When Shannon Theory fits To-Date Data of New Infections

It is well-known that Coronavirus has been propagated due to human activities mainly based at intercontinental flights. Thus, in the first months of 2020, most new countries have already presented peaks in the number of infections, so that airports and borders were closed. With the social restrictions imposed along the beginning of second semester of 2020, the curve of cases of infections has exhibited to be flat in comparison to the beginning of 2020. Therefore, the human activities of end-of-year 2020 have caused againg peaks as the second wave of the pandemic in most countries. So far, by the end of 2021, most countries particularly located at Europe, are exhibiting the fourth wave. In this paper, the entropy of Shannon is considered as inherent mechanism and responsible of waves and large peaks of the number of infections. Modelling of data, the results of this paper suggest the inherent presence of a global entropy due to the transfer of randomness between neighboring countries. © 2021 IEEE.

Impacts of COVID-19 on Sustainable Development Goals and effective approaches to maneuver them in the post-pandemic environment.

In the pursuit of constructing a sustainable world for all through the instrumental seventeen Sustainable Development Goals, the COVID-19 pandemic emerged and affected the efforts concentrated on these goals. Therefore, there is a pressing need to analyze the extent of the impact that unfolded from the pandemic on each Sustainable Development Goal and further to direct the post-pandemic situation to accelerate the progress in every goal. Besides, there exists a knowledge gap in understanding the Sustainable Development Goals and its interaction with each goal through synergic and trade-off effects. To address the aforementioned imperative problems, this study is formulated to perform an impact assessment as well as to provide direction in the post-pandemic environment to effectively progress towards the Sustainable Development Goals by using a hybrid qualitative and quantitative framework. A detailed investigation is carried out to examine the pandemic impacts in every goal, and a quantified impact analysis is performed in terms of the targets of the Sustainable Development Goals with the aid of ranking methodology. The results indicate that SDG 1 and SDG 8 are the most impacted goal. To provide deeper perspectives into the Sustainable Development Goals, a critical analysis of the targets and indicators is performed to characterize the goals from their elemental point of view, such as nature of goals, depending factors, locus of the goal, and Sustainable Development Goal interactions. Further, a novel parameter, the degree of randomness, is proposed whose application in environmental research is immense. The impact on each goal and impact interaction between all the SDGs are also mapped, through which the dynamics of Sustainable Development Goal interactions is elaborated. In context with the post-pandemic scenario, the strategies to achieve the Sustainable Development Goals with environmental focus are presented with prioritization factor that supports quick recovery. The introduced prioritization factor is formulated by employing a multi-criteria analysis methodology. In addition, the fundamental elements of SDGs are built upon one another to frame an optimized and effective approach to achieving the SDGs in the post-pandemic environment. Despite the strategies, a conceptual framework to align the business practices with the SDGs is propounded. This study deep down would provide a unique perspective to the research community and would impart deeper knowledge in connection with sustainability, while the solutions framed would steer the policy and decision-makers.

A spatial randomness test based on the box-counting dimension.

Statistical modelling of a spatial point pattern often begins by testing the hypothesis of spatial randomness. Classical tests are based on quadrat counts and distance-based methods. Alternatively, we propose a new statistical test of spatial randomness based on the fractal dimension, calculated through the box-counting method providing an inferential perspective contrary to the more often descriptive use of this method. We also develop a graphical test based on the log-log plot to calculate the box-counting dimension. We evaluate the performance of our methodology by conducting a simulation study and analysing a COVID-19 dataset. The results reinforce the good performance of the method that arises as an alternative to the more classical distances-based strategies.

Experiences of Space and Time in the Covid-19 Pandemic: Letter From Bilbao.

The COVID-19 pandemic has become a global phenomenon with powerful effects. The deaths and suffering, and the measures taken to mitigate the propagation, are changing the way we relate, work, and enjoy ourselves. The psychological impact on individuals and groups can only be partially known yet, but deserves examination. We focus on the specific place where we live: the city of Bilbao in northern Spain. The response of its people could represent an example of the COVID-19 experience and its effects, and some aspects might be generalized elsewhere. This paper reflects on the influence of the pandemic on some relevant social dimensions: personal and public spaces, affected by estrangement, time, subject to a slower pace, which pushes us into a different contact with our internal world. It also addresses a social vision of the pandemic as a punishment for our errors in the socio-economic and environmental fields, the role of uncertainty that generates strong defensive movements at the group level, and the expressions of personal and group courage throughout the process. The intensity of the pandemic in Bilbao and many parts of the world still prevents reflection on the long-term effects, which will have to be investigated in the future.

Losing the Home Field Advantage When Playing Behind Closed Doors During COVID-19: Change or Chance?

Due to restrictions against the COVID-19 pandemic, spectators were not allowed to attend soccer matches at the end of the 2019/2020 season. Previous studies suggest that the absence of a home crowd changes the home field advantage in terms of match outcomes, offensive performance, and referee decisions. However, because of the small sample sizes, these changes may be random rather than meaningful. To test this, we created 1,000,000 randomized samples from the previous four seasons with the exact same number of matches played behind closed doors in Europe's four most elite soccer leagues at the end of the 2019/2020 season. We found that across countries (Germany, Spain, Italy, and England), performance indices and referee decisions (except red cards) indeed changed to the detriment of the home team beyond the level of chance. However, this overall pattern could be ascribed to specific countries. Most importantly, the proportion of points won by the home teams declined significantly only in Germany, which was accompanied by a meaningful increase in (1) the proportion of goals scored by the away teams and (2) the proportion of yellow cards given to the home teams. We conclude that the home field advantage may indeed be lost when spectators are absent. However, in future studies, more detailed behavioral analyses are needed to determine the robustness and the behavioral determinants of this phenomenon across leagues and countries.