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This paper is to investigate the extent and speed of the spread of the coronavirus disease 2019 (COVID-19) pandemic in the United States (US). For this purpose, the fractional form of the susceptible-exposed-infected-recovered-vaccinated-quarantined-hospitalized-social distancing (SEIR-VQHP) model is initially developed, considering the effects of social distancing, quarantine, hospitalization, and vaccination. Then, a Monte Carlo-based back analysis method is proposed by defining the model parameters, viz. the effects of social distancing rate (α), infection rate (β), vaccination rate (ρ), average latency period (γ), infection-to-quarantine rate (δ), time-dependent recovery rate (λ), time-dependent mortality rate (κ), hospitalization rate (ξ), hospitalization-to-recovery rate (ψ), hospitalization-to-mortality rate (ϕ), and the fractional degree of differential equations as random variables, to obtain the optimal parameters and provide the best combination of fractional order so as to give the best possible fit to the data selected between January 20, 2020 and February 10, 2021. The results demonstrate that the number of infected, recovered, and dead cases by the end of 2021 will reach 1.0, 49.8, and 0.7 million, respectively. Moreover, the histograms of the fractional order acquired from back analysis are provided that can be utilized in similar fractional analyses as an informed initial suggestion. Furthermore, a sensitivity analysis is provided to investigate the effect of vaccination and social distancing on the number of infected cases. The results show that if the social distancing increases by 25% and the vaccination rate doubles, the number of infected cases will drop to 0.13 million by early 2022, indicating relative pandemic control in the US. [ABSTRACT FROM AUTHOR]

Disasters have occurred in the United States since our inception. Although we cannot know when or how the next disaster will occur, we can take measures to generally prepare for the effects of disasters. One of these measures is individual preparedness. Individuals may purchase power generators for use in the event of long-term power outages or standard AA batteries and a flashlight for short-term power outages. A struggle for both emergency managers and elected officials is convincing individuals that they should take proactive steps to prepare for the eventuality of a disaster. One policy implemented by five states has been Emergency or Disaster Preparedness Sales Tax Holidays. These states have identified a number of items deemed necessary for disaster preparedness and deemed a particular weekend each year as the "sales tax holiday." This means that individuals can purchase the goods identified by the state without paying a state sales tax during that period. The basic thought is that by removing the tax and drawing attention to preparedness, individuals may be more likely to purchase these goods and become more prepared for disaster. In this paper, I seek to understand why these sales tax holidays were enacted in these states. Were they done during a policy window after a major disaster as a reactive measure, or were they done prior to a major event as a proactive measure? An analysis of legislative policies in each state, along with an examination of presidentially declared disasters, will be used to explore these questions. [ABSTRACT FROM AUTHOR]

Uncertainty is known to have negative impact on financial markets through its effects on investors' decisions. In the wake of the "Great Recession", quite a few recent studies have highlighted the role of uncertainty in predicting in-sample movements of interest rates. Since in-sample predictability does not guarantee out-of-sample forecasting gains, in this paper, we used historical daily and monthly data for the US to forecast mean and volatility of interest rate. The results show that changes in uncertainty measure movements fail to add any significant statistical gains to the forecast of interest rates for the US. In other words, policy makers in the US are not likely to improve their accuracy of future movements of the policy rate's mean and volatility by incorporating information derived from changes in metrics of uncertainty. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of computing all possible order ideals and also sets connected to 1, and the corresponding border bases, for the vanishing ideal of a given finite set of points. In this context, two different approaches are discussed: based on the Buchberger–Möller Algorithm [H. M. Möller and B. Buchberger, The construction of multivariate polynomials with preassigned zeros, EUROCAM '82 Conf., Computer Algebra, Marseille/France 1982, Lect. Notes Comput. Sci. 144, (1982), pp. 24–31], we first propose a new algorithm to compute all possible order ideals and the corresponding border bases for an ideal of points. The second approach involves adapting the Farr–Gao Algorithm [J. B. Farr and S. Gao, Computing Gröbner bases for vanishing ideals of finite sets of points, in 16th Int. Symp. Applied Algebra, Algebraic Algorithms and Error-Correcting Codes. AAECC-16, Las Vegas, NV, USA (Springer, Berlin, 2006), pp. 118–127] for finding all sets connected to 1, as well as the corresponding border bases, for an ideal of points. It should be noted that our algorithms are term ordering free. Therefore, they can compute successfully all border bases for an ideal of points. Both proposed algorithms have been implemented and their efficiency is discussed via a set of benchmarks. [ABSTRACT FROM AUTHOR]

Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA. [ABSTRACT FROM AUTHOR]

Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA. [ABSTRACT FROM AUTHOR]

Please send your abstracts (or copies of papers that you want to see reviewed here) to vladik@utep.edu, or by regular mail to Vladik Kreinovich, Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA. [ABSTRACT FROM AUTHOR]