ABSTRACT
The ranking of nodes in a network according to their centrality or "importance” is a classic problem that has attracted the interest of different scientific communities in the last decades. The current COVID-19 pandemic has recently rejuvenated the interest in this problem, as it informs the selection of which individuals should be tested in a population of asymptomatic individuals, or which individuals should be vaccinated first. Motivated by these issues, in this paper we review some popular methods for node ranking in undirected unweighted graphs, and compare their performance in a benchmark realistic network that takes into account the community-based structure of society. In particular, we use the information of the relevance of individuals in the network to take a control decision, i.e., which individuals should be tested, and possibly quarantined. Finally, we also review the extension of these ranking methods to weighted graphs, and explore the importance of weights in a contact network by exhibiting a toy model and comparing node rankings for this case in the context of disease spread. [ FROM AUTHOR] Copyright of International Journal of Control is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
This study investigated classroom interaction in three novice-level Chinese language classrooms at middle and high schools in the US in the spring of 2021. Due to school responses to the COVID-19 pandemic, the participating teachers had shifted from teaching in a face-to-face, in-person classroom to fully online and partially online, hybrid classrooms with some students and the teacher present in the school, and some students attending through the Zoom video conferencing platform. Using complex dynamic systems theory as the theoretical framework, such classrooms were viewed as systems co-adapting in these environments to accomplish classroom goals, including the use of the target language to develop topics. Incidents of participants' use of the target language for topic development were examined for evidence of students' engagement and comprehension. There were two data sources. The first data source was video recordings of classroom interaction during lessons, collected both by the researcher who attended lessons through Zoom, and by the teachers, who recorded their computer desktop view during lessons. The second data source was recordings of stimulated recall sessions with each teacher and with focus groups of participating students, in which short video clips from a recent lesson were shown and discussed. Classroom interaction was analyzed at two scales. At the meso scale, analysis led to identifying Instructional Activities (IAs) in which participants most typically used the target language for topic development. Then, at the micro scale, typical incidents from within these IAs were chosen for Conversation Analysis of how topic development was sustained by the participants and how students displayed engagement and comprehension during those incidents. Findings suggest three IAs, typically led by the teachers with the whole class, were more typical for target language use that led to topic development: small talk, Personalized Questions and Answers and Story Asking (categorized as one IA), and discussing a text which included video clips, images, and reading materials. Five excerpts from these IAs illustrated findings about topic development, engagement, and comprehension at the micro scale. In this study, evidence of engagement was determined from verbal and multimodal actions by students within instructional activities (Jacknick, 2021). Turn-by-turn interactions within IAs were typically prompted by the teacher, whose questions projected student involvement in topic development in one of four ways: appealing to the teacher's prior knowledge about students' lives, taking up a student question or comment to develop a topic, inviting responses from students about their personal opinions, experiences, and imagined ideas, and students' interpretations and predictions regarding prepared texts. Atypically, students pressed for topic development in ways which the teacher initially resisted. In those incidents of interaction, both the teachers and the students were involved with topic development, with typically longer turns by the teachers. The imbalance in interaction may partially reflect the presumed novice proficiency level of the students (ACTFL, 2012). However, despite the differences of turn length and complexity, both the students were observed to take actions which supported topic development and displayed engagement and comprehension. Implications are discussed, such as aspects of online and hybrid interaction and classroom setup, possible implications for teacher education and development, and directions for future classroom research. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
ABSTRACT
COVID-19 is a global health crisis that has had unprecedented, widespread impact on households across the United States and has been declared a global pandemic on March 11, 2020 by World Health Organization (WHO). According to Centers for Disease Control and Prevention (CDC), the spread of COVID-19 occurs through person-to-person transmission i.e. close contact with infected people through contaminated surfaces and respiratory fluids carrying infectious virus. This paper presents a data-driven physics-based approach to analyze and predict the rapid growth and spread dynamics of the pandemic. Temporal and spatial conservation laws are used to model the evolution of the COVID-19 pandemic. We integrate quadratic programming and neural networks to learn the parameters and estimate the pandemic growth. The proposed prediction model is validated through finite-time estimation of the pandemic growth using the total number of cases, deaths and recoveries in the United States recorded from March 12, 2020 until October 1, 2021. © 2022 Elsevier B.V.. All rights reserved.
ABSTRACT
This study investigated classroom interaction in three novice-level Chinese language classrooms at middle and high schools in the US in the spring of 2021. Due to school responses to the COVID-19 pandemic, the participating teachers had shifted from teaching in a face-to-face, in-person classroom to fully online and partially online, hybrid classrooms with some students and the teacher present in the school, and some students attending through the Zoom video conferencing platform. Using complex dynamic systems theory as the theoretical framework, such classrooms were viewed as systems co-adapting in these environments to accomplish classroom goals, including the use of the target language to develop topics. Incidents of participants' use of the target language for topic development were examined for evidence of students' engagement and comprehension. There were two data sources. The first data source was video recordings of classroom interaction during lessons, collected both by the researcher who attended lessons through Zoom, and by the teachers, who recorded their computer desktop view during lessons. The second data source was recordings of stimulated recall sessions with each teacher and with focus groups of participating students, in which short video clips from a recent lesson were shown and discussed. Classroom interaction was analyzed at two scales. At the meso scale, analysis led to identifying Instructional Activities (IAs) in which participants most typically used the target language for topic development. Then, at the micro scale, typical incidents from within these IAs were chosen for Conversation Analysis of how topic development was sustained by the participants and how students displayed engagement and comprehension during those incidents. Findings suggest three IAs, typically led by the teachers with the whole class, were more typical for target language use that led to topic development: small talk, Personalized Questions and Answers and Story Asking (categorized as one IA), and discussing a text which included video clips, images, and reading materials. Five excerpts from these IAs illustrated findings about topic development, engagement, and comprehension at the micro scale. In this study, evidence of engagement was determined from verbal and multimodal actions by students within instructional activities (Jacknick, 2021). Turn-by-turn interactions within IAs were typically prompted by the teacher, whose questions projected student involvement in topic development in one of four ways: appealing to the teacher's prior knowledge about students' lives, taking up a student question or comment to develop a topic, inviting responses from students about their personal opinions, experiences, and imagined ideas, and students' interpretations and predictions regarding prepared texts. Atypically, students pressed for topic development in ways which the teacher initially resisted. In those incidents of interaction, both the teachers and the students were involved with topic development, with typically longer turns by the teachers. The imbalance in interaction may partially reflect the presumed novice proficiency level of the students (ACTFL, 2012). However, despite the differences of turn length and complexity, both the students were observed to take actions which supported topic development and displayed engagement and comprehension. Implications are discussed, such as aspects of online and hybrid interaction and classroom setup, possible implications for teacher education and development, and directions for future classroom research. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
ABSTRACT
Purpose: We explored two complex phenomena associated with effective education. First, teachers' professional agency, the volitional actions they take in response to perceived opportunities, was examined to consider individual differences in its enactment. Second, "strong" emotions have been proposed as important in teaching and learning, and we wished to clarify which basic emotions might be involved, besides curiosity, which is a known emotional factor in engagement in teaching. We also explored how agency and basic emotions might be related. Approach: Thirteen teachers working in Scottish secondary schools were interviewed at the start of the covid pandemic in 2020 to discuss relevant feelings, thoughts and actions arising from unprecedented changes in their lives and professional practices. Thematic analysis was used to identify aspects of agentic behavior and basic emotions expressed. Findings: Teacher agency was expressed through adaptability, collective agency, constrained agency, and non-action. Four basic emotion percepts were identified, which we label as "CARE", "CURIOSITY", "COOPERATION", and "CHALLENGE". Originality: We extend the definition of agency to include volitional non-action as a response to opportunity. In contrast to prior research emphasizing emotions as an outcome of volitional behavior, we explore emotions preceding agency. We develop four theoretical propositions related to teacher emotions. (1) Four emotion percepts substantially influence teachers' voluntary motivated behavior. (2) The amount and proportion of emotions experienced varies between individual teachers. (3) The four percepts are experienced concurrently or in rapid succession in engaged teaching contexts. (4) Professional experience and specific situational factors also influence teachers' behavioral choices. For future consideration, we suggest that awareness of emotion percepts may encourage both teachers' engagement and their professional agency for the benefit of their pedagogical practice and outcomes for their students.
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This study investigates additional language development in five plurilingual speakers of English as a second language (L2) during the COVID-19 pandemic and the consequent social distancing. From the perspective of language as a Complex Dynamic System (DE BOT;LOWIE;VERSPOOR, 2007;LARSEN-FREEMAN;CAMERON, 2008;VERSPOOR;DE BOT;LOWIE, 2011), this longitudinal study analyzes the development of a positive Voice Onset Time pattern among participants throughout 12 weeks, including a six-session intervention of explicit instruction of English phonetic-phonological aspects. From Monte Carlo Simulations (VAN DIJK;VERSPOOR;LOWIE, 2011), our quantitative analysis showed positive peaks of performance, indicating the role of variability in learning and developing new language patterns. The qualitative results regarding the online intervention likewise contributed to both language teaching and remote empirical-experimental research studies. © 2022, Universidade Federal de Minas Gerais, Faculdade de Letras. All rights reserved.
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In this paper, we will consider three deterministic models for the study of the interaction between the human immune system and a virus: the logistic model, the Gompertz model, and the generalized logistic model (or Richards model). A qualitative analysis of these three models based on dynamical systems theory will be performed by studying the local behavior of the equilibrium points and obtaining the local dynamics properties from the linear stability point of view. Additionally, we will compare these models in order to understand which is more appropriate to model the interaction between the human immune system and a virus. Some natural medical interpretations will be obtained, which are available for all three models and can be useful to the medical community.
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Excessive groundwater extraction is believed to be one of the main factors for land subsidence which may be caused by tidal flooding due to the position of the surface which is lower than sea level. Covid-19 pandemic that has occurred in Indonesia since March 2020 has caused changes in water consumption patterns which derives from piped water and groundwater. There are many offices and industries that implement work from home (WFH) makes many buildings have a declining occupancy rate. With the decrease in the occupancy rate of the WFH policy, there will be a possibility that groundwater consumption from high-rise buildings that draw groundwater from deep aquifers can be reduced. This research is in the form of modelling and simulation that is used to build a level of understanding on a whole system as well as the interrelationships and interactions between its constituent variables. The purpose of this research was to determine the effect of groundwater consumption during Covid-19 pandemic on land subsidence in Jakarta using the dynamic system simulation method. The results showed that the work from home policy reduces groundwater consumption by 64.7%. In addition, the reduction in groundwater consumption during the Covid-19 pandemic caused land subsidence in Jakarta slows down and the rate of land subsidence in Jakarta decreased from 3.7 cm/year to 1 cm/year.
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The objective of this work is to document the teaching-learning process in the subject of Strategic Business Management in the 4th year of Business Administration in the 2020 academic year, based on the elaboration of a research work, by means of a pedagogical process, where learning is constructed, autonomous and collaborative. For this purpose, the dynamic systems methodology has been used. Based on the dynamic hypothesis and therefore on the establishment of causal relationships, which associate motivation (from different perspectives) of the students/groups and of the teacher with the learning process. The main results achieved, by means of the simulation and the scenarios proposed were the following;i) the model proposed reflects the behavior of the teaching process and its validation is reasonably adjusted to the information and perception of the process and ii) the assumptions in extreme conditions provided relatively reasonable answers.
ABSTRACT
Human emotions and sentiments are dynamic by nature. Nowadays, social networks have become a key resource for human communication and a faithful representation of this dynamism. This fact poses major challenges to those systems addressing sentiment analysis. Therefore, having systems capable of inferring this dynamism has become a key issue. In this paper we introduce Emoweb 2.0, a prototype for dynamic sentiment analysis of Twitter data. A well-known lexicon is taken as starting basis and new words are appended by an unsupervised learning algorithm governing the process. Sentiment values of new words are calculated and dynamically updated depending on the trends detected. Tweet sentiment scores are also computed during the process. A visualization module is included to observe word sentiment fluctuations over time. The experiment performed is based on the ongoing COVID-19 pandemic showing promising results. © 2021 ACM.
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The COVID-19 pandemic brings highly dynamic effects to manufacturing environments, such as frequently shifting markets and unexpected disruptions. Such dynamic environments increase the demand for flexible and real-time manufacturing decision -making strategies. One essential problem is dynamic resource allocation to complete production tasks, especially when a resource disruption (e.g. machine breakdown) occurs. Multi -agent frameworks have been proposed to improve the flexibility and responsiveness of manufacturing systems in a distributed decision -making manner. This work introduces a clustering method based on resource agent (RA) capabilities and an RA coordination strategy that enables dynamic resource reallocation when the manufacturing system is subject to resource disruptions. Copyright (C) 2021 The Authors.
ABSTRACT
As many jurisdictions consider in-person learning strategies (including at Institutions of Higher Education, IHE), implementing travel restrictions or quarantines, and/or establishing interstate pacts to reduce COVID-19 spread, this study explores the degree to which COVID-19 case infection rates in a group of neighboring, Southern and Midwestern U.S. states (namely, Arkansas and its contiguous neighbors) are patterned in a non-random way known as synchrony. Utilizing surrogate synchrony (SUSY) to estimate the dyadic coupling between the COVID-19 case infection rate processes in this region from March to December 2020, results indicate that significant synchrony is present between Arkansas and three of its neighbors. The highest level of instantaneous synchrony occurs between Arkansas and Tennessee, with the next highest level occurring between Arkansas and Missouri. There is evidence of directionality in the synchrony, indicating that Arkansas case infection rates lead Mississippi while rates in Missouri and Tennessee lead Arkansas. The lagged cross-correlations suggest the greatest synchrony to occur between 3 and 6 days. To explore the effect of IHE reopening on COVID-19, synchrony is compared between pre- and post-reopening windows. Results suggested that, following reopening, there are gains in detectable synchrony and that COVID-19 is in-flowing to Arkansas from all of its neighboring states. Taken together, results suggest that there is spatiality to COVID-19 with neighboring states having case infection rates that are significantly synchronous at a lag time that would be expected based on symptom onset. This synchrony is potentially strengthened by the in-flow and cross-border movement of IHE students.
Subject(s)
COVID-19 , Arkansas , Humans , Quarantine , SARS-CoV-2 , Tennessee/epidemiologyABSTRACT
One of the common misconceptions about COVID-19 disease is to assume that we will not see a recurrence after the first wave of the disease has subsided. This completely wrong perception causes people to disregard the necessary protocols and engage in some misbehavior, such as routine socializing or holiday travel. These conditions will put double pressure on the medical staff and endanger the lives of many people around the world. In this research, we are interested in analyzing the existing data to predict the number of infected people in the second wave of out-breaking COVID-19 in Iran. For this purpose, a model is proposed. The mathematical analysis corresponded to the model is also included in this paper. Based on proposed numerical simulations, several scenarios of progress of COVID-19 corresponding to the second wave of the disease in the coming months, will be discussed. We predict that the second wave of will be most severe than the first one. From the results, improving the recovery rate of people with weak immune systems via appropriate medical incentives is resulted as one of the most effective prescriptions to prevent the widespread unbridled outbreak of the second wave of COVID-19.