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Caldeira-Leggett product versus abdominal initio prospective: A new vibrational spectroscopy check water solvation.
The predicted HPs were classified into various classes such as enzymes, transporters, binding proteins, cell division, cell regulatory and other proteins. The outcome of the study could be helpful to understand the molecular mechanism in bacterial pathogenesis and also provide an insight into the identification of potential targets for drug and vaccine development.The Fourier representations (FRs) are indispensable mathematical formulations for modeling and analysis of physical phenomena and engineering systems. This study presents a new set of generalized Fourier representations (GFRs) and phase transforms (PTs). The PTs are special cases of the GFRs and true generalizations of the Hilbert transforms. In particular, the Fourier transform based kernel of the PT is derived and its various properties are discussed. The time derivative and integral, including fractional order, of a signal are obtained using the GFR. It is demonstrated that the general class of time-invariant and time-variant filtering operations, analog and digital modulations can be obtained from the proposed GFR. A narrowband Fourier representation for the time-frequency analysis of a signal is also presented using the GFR. A discrete cosine transform based implementation, to avoid end artifacts due to discontinuities present in the both ends of a signal, is proposed. A fractional-delay in a discrete-time signal using the FR is introduced. The fast Fourier transform implementation of all the proposed representations is developed. Moreover, using the analytic wavelet transform, a wavelet phase transform (WPT) is proposed to obtain a desired phase-shift in a signal under-analysis. A wavelet quadrature transform (WQT) is also presented which is a special case of the WPT with a phase-shift of π / 2 radians. Thus, a wavelet analytic signal representation is derived from the WQT. Theoretical analysis and numerical experiments are conducted to evaluate effectiveness of the proposed methods.Recent research interest has focused on the impact of the room sharing business model on housing markets. However, existing empirical evidence is limited and exclusively focuses on few large cities in the U.S. This study examines the effects of Airbnb on housing rental and sales prices using a unique large-scale dataset comprised of housing market transaction records and the number of Airbnb listings drawn from their website in Taiwan. We estimate a fixed effect model of housing rental and sales price equations and find that a one-standard deviation increase in the number of Airbnb listings raises house rental prices by 0.38%. This finding suggests that a substitution effect is present between Airbnb's short-term accommodation and the housing rental market. Moreover, a larger effect on rental price is found among Airbnb listings that offer an entire room or apartment. Additionally, since September 2017, multinational digital platform companies must comply with a new sales tax policy in Taiwan. We evaluate the effect of this tax policy using the difference-in-difference method and find a negative impact on the number of Airbnb listings and housing rental prices after its implementation. This study is the first to empirically assess the effectiveness of tax policy on regulating room sharing business models.While COVID-19 is rapidly propagating around the globe, the need for providing real-time forecasts of the epidemics pushes fits of dynamical and statistical models to available data beyond their capabilities. Here we focus on statistical predictions of COVID-19 infections performed by fitting asymptotic distributions to actual data. By taking as a case-study the epidemic evolution of total COVID-19 infections in Chinese provinces and Italian regions, we find that predictions are characterized by large uncertainties at the early stages of the epidemic growth. Those uncertainties significantly reduce after the epidemics peak is reached. Differences in the uncertainty of the forecasts at a regional level can be used to highlight the delay in the spread of the virus. Our results warn that long term extrapolation of epidemics counts must be handled with extreme care as they crucially depend not only on the quality of data, but also on the stage of the epidemics, due to the intrinsically non-linear nature of the underlying dynamics. These results suggest that real-time epidemiological projections should include wide uncertainty ranges and urge for the needs of compiling high-quality datasets of infections counts, including asymptomatic patients.In this study, we present a general formulation for the optimal control problem to a class of fuzzy fractional differential systems relating to SIR and SEIR epidemic models. Abiraterone chemical structure In particular, we investigate these epidemic models in the uncertain environment of fuzzy numbers with the rate of change expressed by granular Caputo fuzzy fractional derivatives of order β ∈ (0, 1]. Firstly, the existence and uniqueness of solution to the abstract fractional differential systems with fuzzy parameters and initial data are proved. Next, the optimal control problem for this fractional system is proposed and a necessary condition for the optimality is obtained. Finally, some examples of the fractional SIR and SEIR models are presented and tested with real data extracted from COVID-19 pandemic in Italy and South Korea.It is critical for the recovery of manufacturing industry against COVID-19 by analyzing its impact from supply chain perspective and exploring corresponding countermeasures. Firstly, this paper studies the initial impact caused by worldwide spread of the coronavirus, such as production disruption of raw material and spare parts, unsatisfied market demand due to setbacks in logistics, increasing bankruptcy risk for small and mediumsized enterprises (SMEs), and demand fluctuation enlargement. Secondly, the aftershock of COVID-19 is analyzed. With the trend of regionalization and digitalization, two-step countermeasures are proposed to help the recovery of manufacturing industry within the pandemic and better prepare for the post-COVID-19 world from supply chain perspective.
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