Reflexivity and Practice in COVID-19: Qualitative Analysis of Student Responses to Improvisation in Their Research Methods Course

The improvisations needed to adapt to COVID-19 teaching and learning conditions affected students and faculty alike. This study uses chaos theory and improvisation to examine an undergraduate communication research methods course that was initially delivered synchronously/face-to-face and then transitioned to asynchronous/online in March 2020. Reflective writings were collected at the end of the semester with the 25 students enrolled in the course and follow-up interviews conducted with six students. Thematic analysis revealed that available and attentive student-participant, student-student, and student-instructor communication complemented learner-centered and person-centered goals, but unavailable or inattentive communication, especially with participants and students in the research team, contributed to negative perceptions of learner-centered goals. Implications explore how communication research methods pedagogy may achieve greater available, attentive, and learner/person-oriented goals through modeling, resourcing, reflexivity, and appreciation in online and offline course delivery to enhance shifts in communication pedagogy, whether voluntarily or involuntarily initiated by faculty.

Conclusion

The current (and largely unforeseen) COVID-19 pandemic highlights the value of scenario analysis as a complementary exercise to standard, extrapolative prediction. In this chapter, we review our main findings for geopolitical scenario analysis in general, and for Antarctic geopolitical futures in particular. We conclude that the Antarctic Treaty promotes effective governance of a region described in the Madrid Protocol as ‘a natural reserve devoted to peace and science'. We hope to have shown that a classical geopolitical lens is important and relevant to the study of Antarctic futures. On the specific topic of militarisation, we identified some key driving forces for change and equilibrium. How well the ATS responds to these driving forces will turn on its resilience as a governance system. By this, we mean ‘a capacity to prepare for, to respond to, or bounce back from problems or perturbations and disturbances, which cannot necessarily be predicted or foreseen in advance' (Chandler and Coaffee 2017). As we have seen, scenarios are useful in this zone beyond standard prediction—provided they are plausible, rigorous, and robust. It is our hope that like-minded Parties and researchers might collaborate in scenario work, to contribute to the resilience of the ATS in the challenging years ahead. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Theological Reflections on the Pandemic: A Journal of Bible and Theology

As a response to communities of faith that are trying to make sense of the COVID-19 pandemic, this essay explores biblical traditions about "chaos” represented by primordial waters and monsters that disturb the created order. The essay begins with a summary of three biblical portrayals of chaos: chaos as integral to the created world, chaos as mystery, and chaos intensified by human rebellion. The discussion then weighs divine sovereignty and human responsibility and accepts that chaos is a part of life that challenges humans to work to make this world a better place. Among many possible responses to the chaos presented by the pandemic, this article will focus on lament, fear and trust, and repentance.

A statistical moment-based spectral approach to the chance-constrained stochastic optimal control of epidemic models

This paper presents a spectral approach to the uncertainty management in epidemic models through the formulation of chance-constrained stochastic optimal control problems. Specifically, a statistical moment-based polynomial expansion is used to calculate surrogate models of the stochastic state variables of the problem that allow for the efficient computation of their main statistics as well as their marginal and joint probability density functions at each time instant, which enable the uncertainty management in the epidemic model. Moreover, the surrogate models are employed to perform the corresponding sensitivity and risk analyses. The proposed methodology provides the designers of the optimal control policies with the capability to increase the predictability of the outcomes by adding suitable chance constraints to the epidemic model and formulating a proper cost functional. The chance-constrained optimal control of a COVID-19 epidemic model is considered in order to illustrate the practical application of the proposed methodology.

Optimizing the dynamics of bone turnover with genetic algorithm.

Control systems and the modeling strategies are not only limited to engineering problems. These approaches can be used in the field of bio-mathematics as well and modern studies have promoted this approach to a great extent. The computational modeling and simulation of bone metastasis is painful yet critical after cancer invades the body. This vicious cycle is complex, and several research centers worldwide are devoted to understanding the dynamics and setting up a treatment strategy for this life-threatening behavior of cancer. Cancerous cells activation and the corresponding process of metastasis is reported to boost during the periodic waves of COVID-19, due to the inflammatory nature of the infection associated with SARS-2 and its variants. The bone cells are comprised of two types of cells responsible for bone formation and resorption. The computational framework of such cells, in spatial form, can help the researchers forecast the bone dynamics in a robust manner where the impact of cancer is incorporated into the computational model as a source of perturbation. A series of computational models are presented to explore the complex behavior of bone metastasis with COVID-19 induced infection. The finite difference algorithm is used to simulate the nonlinear computational model. The results obtained are in close agreement with the experimental findings. The computational results can help explore the vicious cycle's fate and help set up control strategies through drug therapies.

Transitions in patterns of caregiver involvement before and during the COVID-19 pandemic: A latent transition analysis.

This retrospective study investigated transitions in patterns of caregiver involvement before and during COVID-19 and their antecedents and consequences. A total of 504 young children (age: M ± SD = 49.92 ± 4.30 months) and their primary caregivers were recruited from the junior classes of 10 preschools in Zhengzhou City, Henan Province, China. Latent profile analysis identified three profiles characterized by (1) high levels of caregiver involvement (HCI), (2) average levels of caregiver involvement (ACI), and (3) low levels of caregiver involvement (LCI). Latent transition analysis showed that caregivers who belonged to the HCI or LCI latent status before COVID-19 tended to transition to the ACI latent status during COVID-19. Higher levels of caregiver depression contributed to a higher probability of transitioning from the HCI to the ACI latent status, while higher levels of household chaos predicted a higher probability of transitioning from the HCI to the ACI latent status and a lower probability of transitioning from the LCI to the ACI latent status. Finally, the transitions in patterns of caregiver involvement were associated with young children's approaches to learning during the pandemic.

The complex evolution of information quality improvement in competitive market

Information is important market resource. High-quality information is beneficial to increase enterprise's reputation and reduce consumer's verification cost. This paper constructs a two-layer dynamic model, in which enterprises simultaneously conduct price and information game. The goal of profit maximization integrates two types of games into one system. The complex evolution of the two-layer system are studied by equilibrium analysis, stability analysis, bifurcation diagram, entropy and Lyapunov exponent. It is found that improving the information quality through regulations will increase involution and reduce stability of the market. Then, the block chain technology is introduced into the model for improving information quality of the market. It is found that increasing enterprises' willingness to adopt block chain can improve the information quality quickly and effectively, and that is verified by entropy value. Therefore, the application and promotion of new technologies are more effective than exogenous regulations for improving information quality in market.

Accurate and fast clade assignment via deep learning and frequency chaos game representation.

BACKGROUND: Since the beginning of the coronavirus disease 2019 pandemic, there has been an explosion of sequencing of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, making it the most widely sequenced virus in the history. Several databases and tools have been created to keep track of genome sequences and variants of the virus; most notably, the GISAID platform hosts millions of complete genome sequences, and it is continuously expanding every day. A challenging task is the development of fast and accurate tools that are able to distinguish between the different SARS-CoV-2 variants and assign them to a clade. RESULTS: In this article, we leverage the frequency chaos game representation (FCGR) and convolutional neural networks (CNNs) to develop an original method that learns how to classify genome sequences that we implement into CouGaR-g, a tool for the clade assignment problem on SARS-CoV-2 sequences. On a testing subset of the GISAID, CouGaR-g achieved an $96.29\%$ overall accuracy, while a similar tool, Covidex, obtained a $77,12\%$ overall accuracy. As far as we know, our method is the first using deep learning and FCGR for intraspecies classification. Furthermore, by using some feature importance methods, CouGaR-g allows to identify k-mers that match SARS-CoV-2 marker variants. CONCLUSIONS: By combining FCGR and CNNs, we develop a method that achieves a better accuracy than Covidex (which is based on random forest) for clade assignment of SARS-CoV-2 genome sequences, also thanks to our training on a much larger dataset, with comparable running times. Our method implemented in CouGaR-g is able to detect k-mers that capture relevant biological information that distinguishes the clades, known as marker variants. AVAILABILITY: The trained models can be tested online providing a FASTA file (with 1 or multiple sequences) at https://huggingface.co/spaces/BIASLab/sars-cov-2-classification-fcgr. CouGaR-g is also available at https://github.com/AlgoLab/CouGaR-g under the GPL.

Forward-backward and period doubling bifurcations in a discrete epidemic model with vaccination and limited medical resources.

A discrete epidemic model with vaccination and limited medical resources is proposed to understand its underlying dynamics. The model induces a nonsmooth two dimensional map that exhibits a surprising array of dynamical behavior including the phenomena of the forward-backward bifurcation and period doubling route to chaos with feasible parameters in an invariant region. We demonstrate, among other things, that the model generates the above described phenomena as the transmission rate or the basic reproduction number of the disease gradually increases provided that the immunization rate is low, the vaccine failure rate is high and the medical resources are limited. Finally, the numerical simulations are provided to illustrate our main results.

Towards nonmanifest chaos and order in biological structures by means of the multifractal paradigm

In a multifractal paradigm of motion, nonlinear behaviors of biological structures (virus systems) of Schrödinger-type regimes at various scale resolutions are analyzed. Then, in the stationary case of these regimes, the functionality of a hidden symmetry of SL(2R) type implies, through a Riccati-type gauge, different synchronization modes among these virus systems. Moreover, assuming that the nonmanifest chaos is not present, specific patterns corresponding to the dynamics in the virus systems can be highlighted. In such a framework, utilizing the methods of artificial intelligence, it is proved that, based on the dynamics of certain patterns, the modifications of the acoustic field can constitute a method of COVID-19 detection. The foundation of the use of artificial intelligence in such a situation is fundamental through the following. The harmonic mapping from the usual measurement space to the matter induces a variational principle, based on which both chaos scenarios and pattern dynamics can be studied. When assimilated to a hyperbolic space, based on which the variational principle works, the initial conditions space permits the generation of a virtual database, based on which the real behaviors of viruses can be shown through a group isomorphism of SL(2R) type. © 2023 Elsevier Inc. All rights reserved.

Chaos and Ideological Strife

This chapter discusses chaos as a philosophic concept and the concept of "constructive chaos” that occurs in the Middle East as an extension of global confusion in the core of the capitalist system. This "constructive chaos” is discussed through the lens of the Arab uprisings that began in 2011. Where other kinds of chaos happen in the most developed societies, which I refer to as the ‘quiet chaos,' and the role of the COVID-19 pandemic to bring this kind of chaos to the cultural forefront. This chapter discusses the indicators of a possible ideological conflict that could occur in the MENA, and may even extend to countries beyond the region. Involving Artificial Intelligence (AI) as a tool in such conflicts tells two stories. The former outlining the war on the mind and the second latter understanding the power of perceptions the subsequent need to control them. The power of the perception of man is the main discussion point here. As part of a new order, forms of manipulation that alter political and economic systems are given alongside evidence of global flailing that affects everyone. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Life career skills development in higher education due to Covid-19: A multivariate approach to students' perceptions

This chapter presents the impact of the Covid-19 pandemic on the course of studies and life career skills development of undergraduate students at the University of Macedonia, a mid-sized public Greek University. It describes a multivariate methodology research that investigated how the students, first-hand experienced the unexpected changes from face-to-face on campus to synchronous online education during the lockdown and how they coped with these changes. Change is considered and described as a main component within current life and career trajectories addressing chaotic and unpredictable circumstances while Chaos Theory of Careers (CTC) offers the theoretical background of the chapter. The research followed the mixed methods paradigm: a multilevel embedded sequential explanatory design including a participant selection model and multivariate data analysis methods. A survey (N = 621) was conducted;individual interviews and focus groups' discussions further explained the quantitative findings. The emerging clusters of students revealed similarities in feelings, motivation, adaptation, and life career skills development. The first cluster comprised of older, digitally high-skilled students, with the required technological equipment, adaptable to change, self-regulated, strongly in favor of synchronous online education;in the second cluster were grouped the younger, digitally medium-skilled students, who regularly participated in both modalities, critically recognized the advantages of either one, feeling strongly in favor of a combination;finally, the third cluster included digitally medium-skilled students who found serious difficulty in using online platforms, dissatisfied with social isolation and distant interaction, strongly preferring face-to-face instruction, valuing direct physical contact, social connection, and networking. © 2023 by Ioanna Papavassiliou-Alexiou, Christina Zourna, Nikos Koutsoupias and Aikaterini Papakota.

The theory of technological response and progress in chaos

Purpose: This study aims to develop the first Theory of Technological Response and Progress in Chaos (TRPC) and examine the case of technological development during the COVID-19 pandemic. The research objectives of this study were to: identify the key technologies that act as a response mechanism during the chaos event, specifically in the case of COVID-19;examine how technologies evolve, develop and diffuse in an immediate crisis and a chaotic environment;theorise various types and periods of technological response and progress during the emergence of chaos and the stages that unfold;and develop policy-oriented recommendations and establish technological foundations to address subsequent chaos events. Design/methodology/approach: This study used the grounded theory as a methodology with a mixed-method approach that included quantitative and qualitative methods. The authors used the quantitative method to assist with the qualitative step to build the TRPC theory. Accordingly, this study integrated machine learning and text mining approaches to the qualitative data analysis following the steps of the grounded theory approach. Findings: As a result of the TRPC theory development process, the authors identified three types of technologies (survival, essential and enhancement technologies) and five types of periods (stable, initial, survival-dominant, essential-dominant and enhancement-dominant periods) that are specific to chaos-technology interactions. The policy implications of this study demonstrate that a required technological base and know-how must be established before a chaotic event emerges. Research limitations/implications: Concerning the limitations of this study, social media data has advantages over other data sources, such as the examination of dynamic areas and analyses of immediate responses to chaos. However, other researchers can examine publications and patent sources to augment the findings concerning scientific approaches and new inventions in relation to COVID-19 and other chaos-specific developments. The authors developed the TRPC theory by studying the COVID-19 pandemic, however, other researchers can utilise it to study other chaos-related conditions, such as chaotic events that are caused by natural disasters. Other scholars can investigate the technological response and progress pattern in other rapidly emerging chaotic events of an uncertain and complex nature to augment these findings. Practical implications: Following the indications of the OECD (2021a) and considering the study conducted by the European Parliamentary Research Service (Kritikos, 2020), the authors identified the key technologies that are significant for chaos and COVID-19 response using machine learning and text intelligence approach. Accordingly, the authors mapped all technological developments using clustering approaches, and examined the technological progress within the immediate chaos period using social media data. Social implications: The key policy implication of this study concerns the need for policymakers to develop policies that will help to establish the required technological base and know-how before chaos emerges. As a result, a rapid response can be implemented to mitigate the chaos and transform it into a competitive advantage. The authors also revealed that this recommendation overlaps with the model of dynamic capabilities in the literature (Teece and Pisano, 2003). Furthermore, this study recommends that nations and organisations establish a technological base that specifically includes technologies that bear 3A characteristics. These are the most crucial technologies for the survival- and essential-dominant stages. Moreover, the results of this study demonstrate that chaos accelerates technological progress through the rapid adoption and diffusion of technologies into different fields. Hence, nations and organisations should regard this rapid progress as an opportunity and establish the prior knowledge base and technologies before chaos emerges. Originality/value: The authors have contributed to he chaos studies and the relationship between chaos and technological development by establishing the first theoretical foundation using the grounded theory approach, hereafter referred to as the TRPC theory. As part of the TRPC theory, the authors present three periods of technological response in the following sequence: survival technology, essential technology and enhancement technology. Moreover, this study illustrates the evolving technological importance and priorities as the periods of technological progress proceed under rapidly developing chaos. © 2023, Emerald Publishing Limited.

How complex is COVID-19: Spatiotemporal variation of chaotic nature of the virus in Indian states.

Like other pandemics, COVID-19 also created a huge socioeconomic imbalance and distress in people. Often, every pandemic is characterized as chaotic and complex. Hence, the nature of the virus spread and deaths should be analyzed to prepare for the next similar pandemic. In this analysis, the popular and well-known time series in chaos theory is implemented, and the results are deduced for the states of India. The phase space reconstruction algorithm is implemented, and false nearest neighbor (FNN) method is applied to determine the dimensionality, and also Lyapunov exponent of the time series is estimated. The chaotic nature of COVID-19 cases showed a less severe and low complexity, with the FNN dimension range of 3-5, whereas the COVID-19 deaths showed moderate complexity with FNN dimensions 2-7. Policymakers should take action on medical availability in rural states and control people's movement in highly populated areas.

Chaos of COVID-19 Superspreading Events: An Analysis Via a Data-driven Approach

Superspreading has become a key mechanism of COVID-19 transmission which creates chaos. The classical approach of compartmental models may not sufficiently reflect the epidemiological situation amid superspreading events (SSEs). We perform a data-driven approach and recognise the deterministic chaos of confirmed cases. The first derivative (≈difference of total confirmed cases) and the second derivative (≈difference of the first derivative) are used upon SSEs to showcase the chaos. Varying solution trajectories, sensitivity and numerical unpredictability are the chaotic characteristics discussed here. © 2023 Indian Institute of Health Management Research.

Caregivers' joint depressive symptoms and preschoolers' daily routines in Chinese three-generation families: Does household chaos matter?

The benefits of routines for children have been consistently demonstrated in previous literature. However, factors that may confer risks for child routines have seldom been examined, particularly in families where parents and grandparents co-care the children. This study aimed to investigate the associations of parents' and grandparents' depressive symptoms with preschoolers' daily routines in Chinese three-generation families and to determine whether household chaos mediated or moderated the associations. The participants were from 171 urban three-generation families where mothers, fathers, and grandmothers (97 paternal and 74 maternal) were primary caregivers. Mothers, fathers, and grandmothers reported their depressive symptoms at Wave 1; at Wave 2 (during the COVID-19 pandemic), caregivers reported household chaos and child routines. The results revealed that child routines were negatively predicted by parents' joint depressive symptoms rather than grandmothers' depressive symptoms. In the associations, household chaos acted as a mediator rather than a moderator. Specifically, household chaos marginally mediated the associations between parents' and grandmothers' depressive symptoms and child routines only in maternal three-generation families. These findings suggest that in three-generation families, caregivers with more depressive symptoms may elicit more chaotic family environments, which may in turn compromise their children's daily routines.

Effects of the Attachment Video-feedback Intervention (AVI) on parents and children at risk of maltreatment during the COVID-19 pandemic.

BACKGROUND: The global health crisis caused by the COVID-19 pandemic has led to an increase in situations of risk of child abuse and neglect. OBJECTIVE: The objective of this study was to examine whether the Attachment Video-feedback Intervention (AVI) program can improve protective factors (decrease parental stress and household chaos, increase parent-child emotional availability and parental reflective functioning) that may diminish child maltreatment in a group of families at risk for child abuse and neglect during the COVID-19 pandemic. PARTICIPANTS AND SETTING: The sample consisted of 41 children aged between 0 and 5 years (Mage = 35.36 months, SD = 14.65; 85.4 % boys) and their parents (Mage = 35.44, SD = 6.04; 75.6 % mothers). METHODS: The study design incorporated two randomized groups (Intervention group: AVI; Control group: treatment as usual) with pre- and post-test evaluations. RESULTS: In comparison to the control group, parents and children exposed to the AVI showed increases in emotional availability. Parents in the AVI group also presented increases in certainty regarding their child's mental states and reported lower levels of household chaos compared to those of the control group. CONCLUSIONS: The AVI program is a valuable intervention for increasing protective factors in families at risk of child abuse and neglect in times of crisis.

Time Efficient Image Encryption-Decryption for Visible and COVID-19 X-ray Images Using Modified Chaos-Based Logistic Map.

In this pandemic situation, radiological images are the biggest source of information in healthcare and, at the same time, one of the foremost troublesome sources to analyze. Clinicians now-a-days must depend to a great extent on therapeutic image investigation performed by exhausted radiologists and some of the time analyzed and filtered themselves. Due to an overflow of patients, transmission of these medical data becomes frequent and maintaining confidentiality turns out to be one of the most important aspects of security along with integrity and availability. Chaos-based cryptography has proven a useful technique in the process of medical image encryption. The specialty of using chaotic maps in image security is its capability to increase the unpredictability and this causes the encryption robust. There are large number of literature available with chaotic map; however, most of these are not useful in low-precision devices due to their time-consuming nature. Taking into consideration of all these facts, a modified encryption technique is proposed for 2D COVID-19 images without compromising security. The novelty of the encryption procedure lies in the proposed design which is split into mainly three parts. In the first part, a variable length gray level code is used to generate the secret key to confuse the intruder and subsequently it is used as the initial parameter of both the chaotic maps. In the second part, one-stage image pixels are shuffled using the address code obtained from the sorting transformation of the first logistic map. In the final stage, a complete diffusion is applied for the whole image using the second chaotic map to counter differential and statistical attack. Algorithm validation is done by experimentation with visual image and COVID-19 X-ray images. In addition, a quantitative analysis is carried out to ensure a negligible data loss between the original and the decrypted image. The strength of the proposed method is tested by calculating the various security parameters like correlation coefficient, NPCR, UACI, and key sensitivity. Comparison analysis shows the effectiveness for the proposed method. Implementation statistics shows time efficiency and proves more security with better unpredictability.

Integer cum fractional ordered active-adaptive synchronization to control vasospasm in chaotic blood vessels to reduce risk of COVID-19 infections

Chaotic states of abnormal vasospasms in blood vessels make heart patients more prone to severe infections of COVID-19, eventually leading to high fatalities. To understand the inherent dynamics of such abrupt vasospasms, an N-type blood vessel model (NBVM) subjected to uncertainties is derived in this paper and investigated both in integer order (IO) as well as fractional-order (FO) dynamics. Active-adaptive controllers are designed to synchronize the chaotic turbulence responsible for undesirable fluctuations in diameter and pressure variations of the blood vessel. The FO-NBVM reveals insightful rich dynamics and faster adaptive synchronization compared to its IO model. The practical implications of this work will be useful in analysing chaotic dysfunctionalities of the blood vessel such as vasoconstriction, ischaemia, necrosis, etc. and help in developing control strategies and modular responses for COVID-19 triggered heart diseases. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

A Study of the International Stock Market Behavior During COVID-19 Pandemic Using a Driven Iterated Function System.

We propose a novel approach to visualize and compare financial markets across the globe using chaos game representation (CGR) of iterated function systems (IFS). We modified a fractal method, widely used in life sciences, and applied it to study the effect of COVID-19 on global financial markets. This modified driven IFS approach is used to generate compact fractal portraits of the financial markets in form of percentage CGR (PC) plots and subtraction percentage (SP) plots. The markets over different periods are compared and the difference is quantified through a parameter called the proximity (Pr) index. The reaction of the financial market across the globe and volatility to the current pandemic of COVID-19 is studied and modeled successfully. The imminent bearish and a surprise bullish pattern of the financial markets across the world is revealed by this fractal method and provides a new tool to study financial markets.