Impacts of household vulnerability on hurricane logistics evacuation under COVID-19: The case of U.S. Hampton Roads.

Historical data suggest that when a severe tropical storm or hurricane impacts a community, the vulnerable segment of the population suffers the most severe consequences. With an increased aging population, it is crucial to understand how vulnerability alters evacuation behavior. Emergent variables such as fear of COVID-19 require additional exploration. People afraid of COVID-19 exposure may refuse to evacuate, exposing themselves unnecessarily. Differentiation is critical to evacuation logistics since it is needed to determine what proportion would stay in a local shelter, public or other, rather than evacuating or staying in their home and guide the logistics resource allocation process. This research uses data from a web and phone survey conducted in the Hampton Roads area of U.S. Virginia, with 2,200 valid responses to analyze the influence of social and demographic vulnerability factors and risk perception on evacuation decisions. This research contributes to the existing literature by developing a multinomial order logit model based on vulnerability factors and intended evacuation decisions, including staying at home, looking for a shelter, or leaving the Hampton Roads area. Findings show that race and risk perception are the variables that influence the decision-making process the most. Fear of COVID-19 transmission is also associated with an increased likelihood of leaving homes during evacuation. The variations in findings from previous studies are discussed regarding their implications for logistics emergency managers.

A review on epidemic models in sight of fractional calculus

Biomathematics has become one of the most significant areas of research as a result of interdisciplinary study. Chronic diseases sometimes referred to as non-communicable and communicable diseases, are conditions that develop over an extended period as a result of different factors like genetics, lifestyle, and environment. The most important common types of disease are cardiovascular, alcohol, cancer, and diabetes. More than three-quarters of the world's (31.4 million) deaths occur in low- and middle-income nations, which are disproportionately affected by different infections. Fractional Calculus is a prominent topic for research within the discipline of Applied Mathematics due to its usefulness in solving problems in many different branches of science, engineering, and medicine. Recent researchers have identified the importance of mathematical tools in various disease models as being very useful to study the dynamics with the help of fractional and integer calculus modeling. Due to the complexity of the underlying connections, both deterministic and stochastic epidemiological models are founded on an inadequate understanding of the infectious network. Over the past several years, the use of different fractional operators to model the problem has grown, and it is now a common way to study how epidemics spread. Recently, researchers have actively considered fractional calculus to study different diseases like COVID-19, cancer, TB, HIV, dengue fever, diabetes, cholera, pine welts, smoking and heart attacks, etc. With the help of fractional operator, we modified a mathematical model for the dynamical transmission, analysis, treatment, vaccination, and precaution leveling necessary to mitigate the negative impact of illness on society in the long run, overcoming the memory effect without defining or considering others parameters. In this review paper, we considered all the recent studies based on the fractional modeling of infectious and non-infectious diseases with different fractional operators such as Caputo, Caputo Fabrizio, ABC, and constant proportional with Caputo, etc. This review paper aims to bring all the information together by considering different fractional operators and their uses in the field of infectious disease modeling. The steps taken to accomplish the goal were developing a mathematical model, identifying the equilibrium point, figuring out the minimal reproductive number, and assessing the stability around the equilibrium point. For future direction, we consider the cancer model to study the growth cells of cancer and the impact of therapy to control infections. An equilibrium solution and an analysis of the behavior dynamics of the cell spread with treatment in the form of chemotherapy were obtained. The simulation shows that the population of cancer cells is influenced by the pace of cancer cell growth with the Caputo fractional derivative. The acquired results show how effective and precise the suggested approach is in helping to better understand how chemotherapy works. Chemotherapy medications have been found to increase immunity against particular cancer by reducing the number of tumor cells. Further, we suggested some future work directions with the help of the new hybrid fractional operator. Our innovative methodology might have significant effects on global stakeholders, policymakers, and national health systems. The current strategies for controlling outbreaks and the vaccination and prevention policies that have been implemented would benefit from a more accurate representation of the dynamics of contagious diseases, which necessitates the development of highly complex mathematical models. Microorganisms, interactions between individuals or groups, and environmental, social, economic, and demographic factors on a broader scale are all examples.

Opportunity to Interpret: Infection Rates in the COVID-19 Pandemic

The COVID-19 pandemic provided students a rare opportunity to use their mathematical knowledge to make sense of a top-of-mind crisis. Based on a report in a major regional newspaper, we designed tasks that require an understanding of infection rates and an interpretation of a misleading claim made in the newspaper. Our analysis of 91 undergraduate students' responses to one of the tasks shows that 77% of the participants used the first or second derivative to interpret the claim. While an assessment for fundamental calculus courses may include both the memorization of procedures and high-cognitive-demand tasks, the findings suggest that it is feasible and worthwhile to build assessment questions on a meaningful connection with the real world. [ FROM AUTHOR] Copyright of Primus: Problems, Resources & Issues in Mathematics Undergraduate Studies 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.)

The Adoption of Contact-Tracing Applications and the Integration of a Health Pass: A Prosocial Rationality in the Privacy Calculus?

Contact tracing applications (CTAs) have been presented as important tools in the fight against the Covid-19 pandemic. In France, the government developed the ‘StopCovid' CTA which later became ‘TousAntiCovid.' This research aims at understanding the determinants of the use of this CTA and of the intention to integrate the health pass. To do that, this study focuses on the perceived value of its use based on the privacy calculus theory. A quantitative study was conducted using a sample of 779 French people. The results show that the use of the CTA and the intention to integrate the health pass are influenced, as hypothesized, by perceived value, distrust towards the government, and personal innovativeness. Perceived value is positively influenced by social and individual benefits, as well as social influence, and, to a lesser extent, negatively influenced by social risks but not by individual risks.

Comparing the effects of motivation in flipped and direct instruction for mastery learning in college precalculus

A lack of mathematics facility prevents many students from pursuing majors in science, technology, engineering, and mathematics. Research revealed that teaching methodology is crucial for success in any course. This dissertation focuses on learners' experiences in a flipped instructional model and a customized direct instructional model. Although the flipped instruction model is gaining popularity among teachers of secondary and post-secondary schools due to permeating access to the internet and digital technology, especially during the recent global Covid-19 pandemic, the flipped teaching method is faced with resistance from both instructors and students. The COVID-19 global pandemic has inspired the education community to rethink the way we teach college courses by promoting active learning strategies for equitable learning, including all variants of blended learning or hybrid instruction. This study investigates the prospects of pedagogical methods (flipped instructional model vs direct instructional model) on college students' course satisfaction, mastery learning, and long-term academic achievement. In total, 90 undergraduate students participated in the study;33 students were taught precalculus using flipped instruction, and 57 received direct instruction. The sequential explanatory mixed methods design (Creswell & Clark, 2007;Teddlie & Tashakkori, 2009) was used to determine predictive abilities of the instructional methods on learning and achievement, and course satisfaction, after controlling for the learner's cognitive and affective background characteristics. The dissertation further explores students' perceptions of the factors affecting their motivation to learn and succeed in Precalculus regardless of the type of teaching method they received. This study considered the effects of undergraduates' mindsets and motivation beliefs, teacher and teaching qualities, experiential behavior of the student, the curriculum, and other factors on their academic performances in introductory college mathematics by collecting, analyzing, and interpreting data from self-reporting surveys, semi-structured interviews, analytical memos of classroom observations, Precalculus and Calculus1 grades, course evaluation, and artifacts of educational activities. The findings indicated that the flipped instructional model supports short-term learning achievement, while the direct instructional model instruction facilitates learning retention. Course satisfaction ratings were comparable. The study also identified three types of mindsets (i.e., a fixed, a growth, or a mixed mindset) and fourteen factors that impacted achievement in the course. This study found that the quality of the learning space, course organization and structure, student's aptitude based on his/her background knowledge, mindsets, motivation beliefs, and teacher's expertise and relationship with the students impacted learning in a course. The ability to harmonize the identified factors affecting student motivation to learn is the culmination of effective learning and success. For example, this dissertation study revealed that mindset beliefs were not diametrical as reported in the literature;rather a student might hold a fixed mindset belief on a topic and become enthusiastic and cognitively engaged on the next. The study's findings provide educators and researchers with evidence to evaluate the implications of students' perceptions of factors affecting their motivation to learn and to succeed in introductory mathematics. The attainment of desired learning outcomes is possible if educators spend time creating quality educational activities that can stimulate the learners' interest to learn, then provide necessary cues to boost their motivation for continued cognitive engagement and participation, as well as guide and support the students to accomplish their desired achievement goals. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Post-Pandemic Sector-Based Investment Model Using Generalized Liouville–Caputo Type

In this article, Euler's technique was employed to solve the novel post-pandemic sector-based investment mathematical model. The solution was established within the framework of the new generalized Caputo-type fractional derivative for the system under consideration that serves as an example of the investment model. The mathematical investment model consists of a system of four fractional-order nonlinear differential equations of the generalized Liouville–Caputo type. Moreover, the existence and uniqueness of solutions for the above fractional order model under pandemic situations were investigated using the well-known Schauder and Banach fixed-point theorem technique. The stability analysis in the context of Ulam—Hyers and generalized Ulam—Hyers criteria was also discussed. Using the investment model under consideration, a new analysis was conducted. Figures that depict the behavior of the classes of the projected model were used to discuss the obtained results. The demonstrated results of the employed technique are extremely emphatic and simple to apply to the system of non-linear equations. When a generalized Liouville–Caputo fractional derivative parameter (ρ) is changed, the results are asymmetric. The current work can attest to the novel generalized Caputo-type fractional operator's suitability for use in mathematical epidemiology and real-world problems towards the future pandemic circumstances.

Utilitarian Ethical Triage Bayesian Decisions With Monetary Value During COVID-19 - A Bayesian Probability Analysis

Utilitarian ethical triage decisions with monetary value are complex and difficult to estimate, with possible benefits for a patient compared to other patients. A triage decision during an emergency combines expected economic value. It includes social and bioethical factors. A new Bayesian approach addresses risk probabilities and improves utilitarian triage decisions. Admission to the ICU (Intensive Care Unit) and the allocation of ventilators for patients depends on a risk-based comorbidity score. It considers the medical prognosis, social factors, personal and social costs. The rankings of the critical factors among patients with predefined ethical treatment success criteria depend on the likelihood of response to treatment and the patient's social circumstances. A sensitivity analysis with regression coefficients shows how the expected monetary value of patents is correlated to make a better judgment. Patient 3 in scenarios 1 and 2 is ranked consistently in priority. Low-ranked patients are placed on a waiting list as the demand for intensive care units increases dramatically with the number of patients infected with COVID-19 or its variants. The problem with utilitarianism ethics is that high net worth patients get an advantage, although disadvantaged patients with social liability are given due consideration. Furthermore, this research introduces a new hedonic Net Present Value based calculus of utilitarian ethics. © 2022 IGI Global. All rights reserved.

APP test system: a case study of calculus

COVID-19 has had a broad impact on society, and a profound impact on education, thus, distance online courses are seen as a way to continue schooling during the pandemic. This study employed Android Studio to develop an APP calculus learning test system which can be used for self-review exercises and allows students to make good use of mobile apps to conduct post-learning and self-testing of calculus at home, and immediately determine their learning results. In addition, through back-end access, teachers can view students' learning scores and the number of wrong and correct questions, and thus, know the effect of individual students' self-review. During the COVID-19 pandemic, as teachers and students cannot interact in class at school, teachers can use the APP calculus learning test system to provide distance remedial teaching to students who fall behind during the course.

Advancing COVID-19 Understanding: Simulating Omicron Variant Spread Using Fractional-Order Models and Haar Wavelet Collocation

This study presents a novel approach for simulating the spread of the Omicron variant of the SARS-CoV-2 virus using fractional-order COVID-19 models and the Haar wavelet collocation method. The proposed model considers various factors that affect virus transmission, while the Haar wavelet collocation method provides an efficient and accurate solution for the fractional derivatives used in the model. This study analyzes the impact of the Omicron variant and provides valuable insights into its transmission dynamics, which can inform public health policies and strategies that are aimed at controlling its spread. Additionally, this study's findings represent a significant step forward in understanding the COVID-19 pandemic and its evolving variants. The results of the simulation showcase the effectiveness of the proposed method and demonstrate its potential to advance the field of COVID-19 research. The COVID epidemic model is reformulated by using fractional derivatives in the Caputo sense. The existence and uniqueness of the proposed model are illustrated in the model, taking into account some results of fixed point theory. The stability analysis for the system is established by incorporating the Hyers–Ulam method. For numerical treatment and simulations, we apply the Haar wavelet collocation method. The parameter estimation for the recorded COVID-19 cases in Pakistan from 23 June 2022 to 23 August 2022 is presented.

Fractional Optimal Control Model of SARS-CoV-2 (COVID-19) Disease in Ghana

Research focus on optimal control problems brought on by fractional differential equations has been extensively applied in practice. However, because they are still open ended and challenging, a number of problems with fractional mathematical modeling and problems with optimal control require additional study. Using fractional-order derivatives defined in the Atangana–Baleanu–Caputo sense, we alter the integer-order model that has been proposed in the literature. We prove the solution's existence, uniqueness, equilibrium points, fundamental reproduction number, and local stability of the equilibrium points. The operator's numerical approach was put into practice to obtain a numerical simulation to back up the analytical conclusions. Fractional optimum controls were incorporated into the model to identify the most efficient intervention strategies for controlling the disease. Utilizing actual data from Ghana for the months of March 2020 to March 2021, the model is validated. The simulation's results show that the fractional operator significantly affected each compartment and that the incidence rate of the population rose when v≥0.6. The examination of the most effective control technique discovered that social exclusion and vaccination were both very effective methods for halting the development of the illness.

"To our health!” Perceived benefits offset privacy concerns in using national contact-tracing apps

PurposeCommunity health is placed under the limelight during the COVID-19 crisis, providing a unique context for investigating citizens' health-privacy tradeoff in accepting social surveillance technology. To elucidate this tradeoff dilemma, an extended privacy calculus framework integrated with the Health Belief Model, legislative protection, and individual collectivism was examined using the case of national contact-tracing apps.Design/methodology/approachThe hypotheses were tested through PLS-SEM analysis with data collected from a survey on Bluezone – a national app in Vietnam.FindingsThe results indicated the negative impact of privacy concerns, which was offset by the positive effect of perceived benefits in using contact-tracing apps. The effect size of perceived benefits on usage frequency was twice as large as that of privacy concerns. Individual collectivism was revealed as a mitigator of the tradeoff dilemma, as it was positively associated with perceived benefits, whereas legislative protection had no such role. Citizens may perceive legislation protection as invalid when the technologies are developed, implemented, and monitored by the authorities.Originality/valueThe theoretical contributions lie in the extension of the privacy calculus model as well as its application in the context of mobile health apps and surveillance technology. The study empirically corroborated that the privacy calculus theory holds when technologies move along the pervasiveness spectrum. This study also provided actionable insights for policymakers and developers who advocate the mass acceptance of national contact-tracing apps.

Robust autoregressive modeling and its diagnostic analytics with a COVID-19 related application

Autoregressive models in time series are useful in various areas. In this article, we propose a skew-t autoregressive model. We estimate its parameters using the expectation-maximization (EM) method and develop the influence methodology based on local perturbations for its validation. We obtain the normal curvatures for four perturbation strategies to identify influential observations, and then to assess their performance through Monte Carlo simulations. An example of financial data analysis is presented to study daily log-returns for Brent crude futures and investigate possible impact by the COVID-19 pandemic. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Fractional-type integral operators and their applications to trend estimation of COVID-19

In this paper, we construct a novel family of fractional-type integral operators of a function (Formula presented.) by replacing sample values (Formula presented.) with the fractional mean values of that function. We give some explicit formulas for higher order moments of the proposed operators and investigate some approximation properties. We also define the fractional variants of Mirakyan–Favard–Szász and Baskakov-type operators and calculate the higher order moments of these operators. We give an explicit formula for fractional derivatives of proposed operators with the help of the Caputo-type fractional derivative Furthermore, several graphical and numerical results are presented in detail to demonstrate the accuracy, applicability, and validity of the proposed operators. Finally, an illustrative real-world example associated with the recent trend of Covid-19 has been investigated to demonstrate the modeling capabilities of fractional-type integral operators. © 2023 John Wiley & Sons, Ltd.

Studying and Simulating the Fractional COVID-19 Model Using an Efficient Spectral Collocation Approach

We give a theoretical and numerical analysis of a coronavirus (COVID-19) infection model in this research. A mathematical model of this system is provided, based on a collection of fractional differential equations (in the Caputo sense). Initially, a rough approximation formula was created for the fractional derivative of tp. Here, the third-kind Chebyshev approximations of the spectral collocation method (SCM) were used. To identify the unknown coefficients of the approximate solution, the proposed problem was transformed into a system of algebraic equations, which was then transformed into a restricted optimization problem. To evaluate the effectiveness and accuracy of the suggested scheme, the residual error function was computed. The objective of this research was to halt the global spread of a disease. A susceptible person may be moved immediately into the confined class after being initially quarantined or an exposed person may be transferred to one of the infected classes. The researchers adopted this strategy and considered both asymptomatic and symptomatic infected patients. Results acquired with the achieved results were contrasted with those obtained using the generalized Runge-Kutta method.

Privacy Personas for IoT-Based Health Research: A Privacy Calculus Approach.

The reliance on data donation from citizens as a driver for research, known as citizen science, has accelerated during the Sars-Cov-2 pandemic. An important enabler of this is Internet of Things (IoT) devices, such as mobile phones and wearable devices, that allow continuous data collection and convenient sharing. However, potentially sensitive health data raises privacy and security concerns for citizens, which research institutions and industries must consider. In e-commerce or social network studies of citizen science, a privacy calculus related to user perceptions is commonly developed, capturing the information disclosure intent of the participants. In this study, we develop a privacy calculus model adapted for IoT-based health research using citizen science for user engagement and data collection. Based on an online survey with 85 participants, we make use of the privacy calculus to analyse the respondents' perceptions. The emerging privacy personas are clustered and compared with previous research, resulting in three distinct personas which can be used by designers and technologists who are responsible for developing suitable forms of data collection. These are the 1) Citizen Science Optimist, the 2) Selective Data Donor, and the 3) Health Data Controller. Together with our privacy calculus for citizen science based digital health research, the three privacy personas are the main contributions of this study.

Two case studies examining how international graduate teaching assistants built mathematical literacy knowledge within the affordances and constraints of a calculus instructional system

As universities contend with high rates of student attrition from intended STEM majors, due to many students' difficulty in passing entry-level mathematics courses, they must examine the systems they have in place and determine how best to support these students. Historically, graduate teaching assistants (GTAs) are assigned to introductory calculus classes, which serve as undergraduate-level gatekeeping courses. On one hand, this allows for additional instructors to assist mathematics faculty;however, many universities do not provide adequate training in pedagogy for their GTAs. Untrained GTAs may not have the requisite pedagogical content knowledge to teach and support struggling students. GTAs should be prepared to teach both content and disciplinary literacy, so they can help students build mathematical knowledge.This two-case qualitative study examines how GTAs trained in pedagogical content knowledge are able to build mathematical literacy knowledge within a calculus instructional system through the theoretical lenses of Knowledge Building (Scardamalia & Bereiter, 2003), Ecological Systems (Bronfenbrenner, 1977) and Activity Systems (Engstrom, 1987). As acquisition of mathematical literacy and fluency in the mathematics spoken and written registers is imperative for students to progress in higher-level mathematics courses, experienced GTAs are positioned to provide supports for students who work toward achieving proficiency in calculus.Even working within system constraints, such as social distancing mandates during the covid-19 pandemic, these GTAs were able to draw on their pedagogical content knowledge to help remediate students in a hybrid classroom environment. Seven Knowledge Building principles surfaced in both the calculus system and the GTAs' classrooms. Universities should consider providing extensive training in pedagogy for their GTAs to become more effective instructors to help offset high attrition rates of their intended STEM majors. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

A parallel approach to the fractional time delay model for predicting the spread of COVID-19

Due to the importance of forecast accuracy for diseases such as COVID-19, the existence of a mathematical model is particularly important. In this research, first, a model to describe the spread of the COVID-19 pandemic is examined. This model is based on a fractional ordinary differential equation. Then the predictor-corrector numerical method is presented to solve this model. Due to the computational challenge of numerically solving fractional models, a task-parallel approach with coarse granularity is presented to solve this model on shared memory systems. The initial data for testing the proposed approach is the data reported on December 31, 2019 by the Wuhan Municipal Commission of the outbreak of the COVID-19 pandemic in the city of Wuhan, China. The numerical results obtained from the proposed parallel approach show that the speedup of the parallel method compared to the sequential method reaches 2.76 in the prediction of 1000 days. © 2022 IEEE.

The role of privacy and emotion in ARBS continuing use intention

PurposeToday, contactless businesses are becoming part of the "new normal” in daily life. Augmented reality-based services (ARBS) thus provide a mechanism for contactless commerce, offering customers access to sensory experiences, especially during the COVID-19 pandemic. However, privacy can be a key concern when consumers decide whether to continue using ARBS. Thus, drawing on the Appraisal Tendency Framework (ATF), the study aims to examine how augmentation quality (Aug-Q), discrete emotions (joy and frustration) and privacy perceptions influence users' ARBS continuing use intention.Design/methodology/approachA survey methodology with a well-designed online questionnaire was used for data collection. The data were analyzed using a structural equation model with Amos v. 22.0 software.FindingsThis study demonstrated that Aug-Q had a significant positive impact on joy and a significant negative impact on frustration. Additionally, joy was positively associated with the perception of privacy benefits and ARBS continuing use intention, while frustration was negatively associated with the perception of privacy benefits and ARBS continuing use intention. The results also indicate that (perceived privacy risks) PPR–benefits predict the likelihood of ARBS continuing use intention.Originality/valueThis study enhances understanding of users' ARBS continuing use intention from an integrative perspective based on the ATF, thus identifying the Aug-Q-induced emotions that subsequently influence privacy trade-offs and predict users' ARBS continuing use intention. The results provide evidence that privacy and emotions can be key determinants when consumers decide whether to continue using ARBS. The findings of this research may be beneficial for commercial companies in preventing the loss of ARBS users.

Opportunity to interpret: Infection rates in the covid-19 pandemic

The COVIC-19 pandemic provided students a rare opportunity to use their mathematical knowledge to make sense of a top-of-mind crisis. Based on a report in a major regional newspaper, we designed tasks that require an understanding of infection rates and an interpretation of a misleading claim made in the newspaper. Our analysis of 91 undergraduate students' responses to one of the tasks shows that 77% of the participants used the first or second derivative to interpret the claim. While an assessment for fundamental calculus courses may include both the memorization of procedures and high-cognitive-demand tasks, the findings suggest that it is feasible and worthwhile to build assessment questions on a meaningful connection with the real world. (PsycInfo Database Record (c) 2022 APA, all rights reserved)