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The study is aimed at investigating the effects of nuclear fusion reactors, human development, and economic growth on nuclear energy consumption in the United States from 1990 to 2019 using time and frequency causality analyses. The time domain causality analysis examined the relationship between variables over time using a single test statistic, while the frequency domain analysis explored causality in the short and long term at different frequencies. The findings from the time domain analysis indicated that nuclear energy consumption had a unidirectional causal effect on the human development index. Conversely, nuclear fusion reactors had a unidirectional causal impact on nuclear energy consumption. The results from the frequency domain analysis revealed that economic growth had a permanent unidirectional causal effect on nuclear energy consumption. In contrast, nuclear energy consumption had a temporary unidirectional causal impact on the human development index. Additionally, there was a bidirectional temporary and permanent causal effect between nuclear fusion reactors and nuclear energy consumption. Based on these findings, the study recommends that the United States continue providing financial incentives to develop nuclear energy technologies, such as constructing new nuclear power plants and offering subsidies to encourage the use of nuclear energy. [ABSTRACT FROM AUTHOR]

This study develops mathematical tools and approaches to investigate spatiotemporal patterns of firearm acquisition in the U.S. complemented by hypothesis testing and statistical analysis. First, state-level and nation-level instant background check (BC) data are employed as proxy of firearm acquisition corresponding to 1999–2021. The relative-phase time-series of BC in each U.S. state is recovered and utilized to calculate the time-series of the U.S. states' synchronization degree. We reveal that U.S. states present a high-level degree of synchronization except in 2010–2011 and after 2018. Comparing these results with respect to a sitting U.S. president provides additional information: specifically, any two presidential terms are characterized by statistically different synchronization degrees except G. W. Bush's first term and B. H. Obama's second term. Next, to detail variations of BC, short-time Fourier transform, dimensionality reduction techniques, and diffusion maps are implemented within a time-frequency representation. Firearm acquisition in the high frequency band is described by a low-dimensional embedding, in the form of a plane with two embedding coordinates. Data points on the embedding plane identify separate clusters that signify state transitions in the original BC data with respect to different time windows. Through this analysis, we reveal that the frequency content of the BC data has a time-dependent characteristic. By comparing the diffusion map at hand with respect to a presidential term, we find that at least one of the embedding coordinates presents statistically significant variations between any two presidential terms except B. H. Obama's first term and D. J. Trump's pre-COVID term. The results point at a possible interplay between firearm acquisition in the U.S. and a presidential term. [ABSTRACT FROM AUTHOR]

We provide new evidence on the relationship between inflation and its uncertainty in the United States on an historical basis, covering the period from 1775 to 2014. First, we use a bounded approach for measuring inflation uncertainty, as proposed by Chan et al. (2013), and compare the results with the Stock and Watson (2007) and Chan (2015) methods. Second, we employ the wavelet methodology to analyze the comovements and causal effects between the two series. Our results provide evidence of a relationship between inflation and its uncertainty that varies across time and frequency. First, we show that in the medium and long runs, the Freidman–Ball hypothesis holds with a bounded measure of uncertainty, while if the Stock and Watson (2007) measure of uncertainty is used, the Cukierman–Meltzer reasoning prevails. Therefore, the findings are sensitive to the way inflation uncertainty is computed. Second, we discover mixed evidence about the inflation–uncertainty nexus in the short run, findings that explain the mixed results reported to date in the empirical literature. [ABSTRACT FROM AUTHOR]

• Time-frequency analysis of the log files of the HVAD system improved the diagnostic sensitivity to the early stage of the development of thrombotic complications. • Loss of circadian fluctuations in the pump power consumption was identified as a reliable marker to predict the development of pump thrombosis before clinical evidence. • Integrating real-time analysis of circadian variability in the diagnostic software of the HVAD external controller might result in a clinically relevant tool for the early identification of pump thrombosis. • This tool might have remarkable clinical value to improve the results of medical management of pump thrombosis and to decrease the need for emergency pump exchange. No clinical standardized methods exist to identify the early stage of the development of pump thrombosis in the setting of HVAD (Medtronic Inc., USA) implantation. We aimed at developing a clinically relevant tool to evaluate HVAD operation during long-term support and at identifying a new reliable marker for the early diagnosis of pump thrombosis reflecting altered patient-pump physiological interplay. We developed a novel algorithm based on time-frequency analysis of the HVAD log files allowing the detection of the intrinsic circadian rhythmicity of the pump power consumption. With this tool, we retrospectively evaluated (1) post-operative restoration of circadian rhythm (n = 14 patients), (2) long-term stability of circadian rhythmicity in patients with no reported adverse events (n = 12), and (3) alteration of circadian fluctuations in patients who suffered from pump thrombosis (n = 19). We demonstrate (1) progressive development of circadian rhythm following post-operative recovery (93% of the patients, 23 ± 15 days after implantation), (2) long-term stability of circadian rhythmicity in patients with no thrombotic complications (92% of the patients; 962 (445–1447) days of support), and (3) severe instability and loss of circadian fluctuations before the thrombotic event (89% of the patients, 12 ± 6 days ahead of the clinical manifestation of overt pump thrombosis). Furthermore, we provide the first clinical evidence of recovery of circadian rhythmicity following non-surgical resolution of pump thrombosis. Time-frequency analysis of the HVAD log files provides a new tool for the early diagnosis of pump thrombosis. Loss of circadian rhythmicity might trigger medical evaluation, improving the results of medical management of pump thrombosis, and decreasing the need for pump exchange. [ABSTRACT FROM AUTHOR]

This study quantitatively assesses existing explanations for the Feldstein–Horioka puzzle using time–frequency domain analyses for nine countries for the period 1885–2010. The main findings are summarized as follows. First, large economies (e.g. the United States, Italy) show higher correlations between saving and investment than middle‐sized and small countries do. Second, countries can be grouped into two time‐changing patterns of correlations: inverted U‐shaped and increasing patterns. Third, the fiscal balance seems most related to a positive saving‐investment correlation in many countries. Fourth, a global common factor plays an important role in explaining the Feldstein–Horioka puzzle. [ABSTRACT FROM AUTHOR]

Commodity and asset prices have a well-documented effect on economic growth as manifested through various channels. At the same time, the business cycle influences the commodity and asset prices. Whereas empirical evidence on the effect of commodity and asset prices on the long-run economic growth is ambiguous, most of the previous researches highlight a positive correlation in the short run. The aim of this article is to disentangle the short- and long-run co-movements between US historical business cycles and commodity and asset prices over the period 1859–2013. For this purpose, we use a time–frequency approach and we test the historical influence of oil, gold, housing and stock prices over the output growth. In contrast to other studies, we control for the effect of other prices and monetary conditions, using the wavelet partial coherency. In line with the previous works, we discover that co-movements between economic growth and commodity and assets prices manifest especially in the short run. We also find that stock returns and housing prices have a more powerful effect on the US economic growth rate than the oil and gold prices. The long-run co-movements are documented especially around the World War II. Finally, when controlling for the influence of the interest rate, inflation and other commodity and asset prices, co-movements become weaker in the short run. In general, the oil and housing prices lead the GDP growth, the US output leads the gold prices, while there is no clear causality direction between business cycle and stock prices. [ABSTRACT FROM AUTHOR]

We use frequency-domain techniques, namely wavelets and cross-spectra, to examine the association between the daily prices of crude oil futures and daily S&P500 futures closing prices over the past several decades. We investigate contemporaneous and lag–lead relationships in levels and returns. It is our belief that the wavelet and cross-spectral analyses employed in this paper offer insights regarding the relationship between oil prices and stock returns that are not apparent from a conventional time-domain framework. Our findings cast doubt on the purported negative relationship between oil and the U.S. stock market. Our analysis suggests that oil prices lead oil volume, and S&P500 trading volume leads S&P500 prices. [ABSTRACT FROM AUTHOR]

Time-frequency techniques to characterize cuspate patterns in light detection and ranging (lidar) data are introduced using examples from the Atlantic coast of Florida, United States. These techniques permit the efficient study of beach face landforms over many kilometers of coastline at multiple spatial scales. From a lidar image, a beach-parallel spatial series is generated. Here, this series is the shore-normal position of a specific elevation (contour line). Well-established time-frequency analysis techniques, wavelet transforms, and S-Transforms, are then applied to the spatial series. These methods yield results entirely compatible with the traditional method of estimating the spacing of cuspate features. In addition, confidence intervals are readily established for the spatial extent and wavelengths of cuspate landforms simultaneously at multiple scales. Examples show this method is useful for capturing transitions in cuspate shapes. With the advent of land-based time-lapse lidar, such techniques should be particularly useful for characterizing the evolution of cuspate landforms and testing models for beach face dynamics. [ABSTRACT FROM AUTHOR]