Global Power Spectrum Research Articles

Effects of feedback-free starburst galaxies on the 21-cm signal and reionization history

ABSTRACT Different star formation models at Cosmic Dawn produce detectable signatures in the observables of upcoming 21-cm experiments. In this work, we consider the physical scenario of feedback-free starbursts (FFB), according to which the star formation efficiency (SFE) is enhanced in sufficiently massive haloes at early enough times, thus explaining the indication from the JWST for an excess of bright galaxies at $z \ge 10$. We model the contribution of FFBs to popII SFE and compute the impact these have on the 21-cm global signal and power spectrum. We show that FFBs affect the evolution of the brightness temperature and the 21-cm power spectrum, but they only have a limited effect on the neutral hydrogen fraction. We investigate how the observables are affected by changes in the underlying star formation model and by contribution from popIII stars. Finally, we forecast the capability of next-generation Hydrogen Epoch of Reionization Array (HERA) to detect the existence of FFB galaxies via power spectrum measurements. Our results show the possibility of a significant detection, provided that popII stars are the main drivers of lowering the spin temperature. Efficient popIII star formation will make the detection more challenging.

Resting-state networks representation of the global phenomena.

Resting-state functional magnetic resonance imaging (rsfMRI) has been widely applied to investigate spontaneous neural activity, often based on its macroscopic organization that is termed resting-state networks (RSNs). Although the neurophysiological mechanisms underlying the RSN organization remain largely unknown, accumulating evidence points to a substantial contribution from the global signals to their structured synchronization. This study further explored the phenomenon by taking advantage of the inter- and intra-subject variations of the time delay and correlation coefficient of the signal timeseries in each region using the global mean signal as the reference signal. Consistent with the hypothesis based on the empirical and theoretical findings, the time lag and correlation, which have consistently been proven to represent local hemodynamic status, were shown to organize networks equivalent to RSNs. The results not only provide further evidence that the local hemodynamic status could be the direct source of the RSNs' spatial patterns but also explain how the regional variations in the hemodynamics, combined with the changes in the global events' power spectrum, lead to the observations. While the findings pose challenges to interpretations of rsfMRI studies, they further support the view that rsfMRI can offer detailed information related to global neurophysiological phenomena as well as local hemodynamics that would have great potential as biomarkers.

An effective model for the cosmic-dawn 21-cm signal

ABSTRACT The 21-cm signal holds the key to understanding the first structure formation during cosmic dawn. Theoretical progress over the last decade has focused on simulations of this signal, given the non-linear and non-local relation between initial conditions and observables (21 cm or reionization maps). Here, instead, we propose an effective and fully analytical model for the 21-cm signal during cosmic dawn. We take advantage of the exponential-like behaviour of the local star-formation rate density (SFRD) against densities at early times to analytically find its correlation functions including non-linearities. The SFRD acts as the building block to obtain the statistics of radiative fields (X-ray and Lyman α fluxes), and therefore the 21-cm signal. We implement this model as the public python package Zeus21. This code can fully predict the 21-cm global signal and power spectrum in ∼1 s, with negligible memory requirements. When comparing against state-of-the-art semi-numerical simulations from 21CMFAST we find agreement to $\sim 10~{{\ \rm per\ cent}}$ precision in both the 21-cm global signal and power spectra, after accounting for a (previously missed) underestimation of adiabatic fluctuations in 21CMFAST. Zeus21 is modular, allowing the user to vary the astrophysical model for the first galaxies, and interfaces with the cosmological code CLASS, which enables searches for beyond standard-model cosmology in 21-cm data. This represents a step towards bringing 21-cm to the era of precision cosmology.

Optimization study on the production of argemone mexicana biodiesel and its impact on diesel engine combustion stability: Taguchi model and wavelet analysis

A research study is being conducted to investigate the potential use of Argemone mexicana oil (adulterated with mustard oil and non-edible) as a biodiesel source in a compression ignition (CI) engine to replace the maximum amount of fossil fuel in the engine. This research aims to optimize transesterification reaction parameters to achieve high yields of Argemone mexicana biodiesel (AGB). The study employed the Taguchi method to optimize the appropriate combination of transesterification reaction parameters. A transesterification reaction at 60 °C, 90 min, with a molar ratio of 6:1, produced a maximum yield of 95%. Additionally, the present study explores the combustion stability of a 4-cylinder turbocharged CRDI engine using AGB/diesel blended fuels. The wavelet transform (WT) analysis has also been used to observe temporal and spectral variations in the combustion parameters of various AGB/diesel blends. The global wavelet spectrum (GWS) and wavelet power spectrum (WPS) analyses showed that the AB10 blend exhibited the lowest cyclic fluctuations in indicated mean effective pressure (IMEP) combustion parameter at a low engine load. The study of the spectral variations of the IMEP combustion parameter based on the in-cylinder pressure feedback can be used to develop better fuel injection control strategies.

A comparative study of data-driven modal decomposition analysis of unforced and forced cylinder wakes

The present study on the recognition of coherent structures in flow fields was conducted using three typical data-driven modal decomposition methods: proper orthogonal decomposition (POD), dynamic mode decomposition (DMD), and Fourier mode decomposition (FMD). Two real circular cylinder wake flows (forced and unforced), obtained from two-dimensional particle image velocimetry (2D PIV) measurements, were analyzed to extract the coherent structures. It was found that the POD method could be used to extract the large-scale structures from the fluctuating velocity in a wake flow, the DMD method showed potential for dynamical mode frequency identification and linear reconstruction of the flow field, and the FMD method provided a significant computational efficiency advantage when the dominant frequency of the flow field was known. The limitations of the three methods were also identified: The POD method was incomplete in the spatial–temporal decomposition and each mode mixed multiple frequencies leading to unclear physics, the DMD method is based on the linear assumption and thus the highly nonlinear part of the flow field was unsuitable, and the FMD method is based on global power spectrum analysis while being overwhelmed by an unknown high-frequency flow field.

Thermal and reionization history within a large-volume semi-analytic galaxy formation simulation

ABSTRACTWe predict the 21-cm global signal and power spectra during the Epoch of Reionization using the meraxes semi-analytic galaxy formation and reionization model, updated to include X-ray heating and thermal evolution of the intergalactic medium. Studying the formation and evolution of galaxies together with the reionization of cosmic hydrogen using semi-analytic models (such as M eraxes) requires N-body simulations within large volumes and high-mass resolutions. For this, we use a simulation of side-length 210 h−1 Mpc with 43203 particles resolving dark matter haloes to masses of $5\times 10^8 \rm{ }h^{-1}\, \mathrm{M_\odot }$. To reach the mass resolution of atomically cooled galaxies, thought to be the dominant population contributing to reionization, at z = 20 of $\sim 2\times 10^7 \text{ }h^{-1}\, \mathrm{M_\odot }$, we augment this simulation using the darkforest Monte Carlo merger tree algorithm (achieving an effective particle count of ∼1012). Using this augmented simulation, we explore the impact of mass resolution on the predicted reionization history as well as the impact of X-ray heating on the 21-cm global signal and the 21-cm power spectra. We also explore the cosmic variance of 21-cm statistics within 703 h−3 Mpc3 sub-volumes. We find that the midpoint of reionization varies by Δz ∼ 0.8 and that the cosmic variance on the power spectrum is underestimated by a factor of 2–4 at k ∼ 0.1–0.4 Mpc−1 due to the non-Gaussian nature of the 21-cm signal. To our knowledge, this work represents the first model of both reionization and galaxy formation which resolves low-mass atomically cooled galaxies while simultaneously sampling sufficiently large scales necessary for exploring the effects of X-rays in the early Universe.

An empirical nonlinear power spectrum overdensity response

Context.The overdensity inside a cosmological sub-volume and the tidal fields from its surroundings affect the matter distribution of the region. The resulting difference between the local and global power spectra is characterized by the response function.Aims.Our aim is to provide a new, simple, and accurate formula for the power spectrum overdensity response at highly nonlinear scales based on the results of cosmological simulations and paying special attention to the lognormal nature of the density field.Methods.We measured the dark matter power spectrum amplitude as a function of the overdensity (δW) inN-body simulation subsamples. We show that the response follows a power-law form in terms of (1 + δW), and we provide a new fit in terms of the variance,σ(L), of a sub-volume of sizeL.Results.Our fit has a similar accuracy and a comparable complexity to second-order standard perturbation theory on large scales, but it is also valid for nonlinear (smaller) scales, where perturbation theory needs higher-order terms for a comparable precision. Furthermore, we show that the lognormal nature of the overdensity distribution causes a previously unidentified bias: the power spectrum amplitude for a subsample with an average density is typically underestimated by about −2σ2. Although this bias falls to the sub-percent level above characteristic scales of 200 Mpc h−1, taking it into account improves the accuracy of estimating power spectra from zoom-in simulations and smaller high-resolution surveys embedded in larger low-resolution volumes.

Wavelet analysis for cyclic combustion dynamics of a multi-cylinder CRDI diesel engine fuelled with a blending of argemone biodiesel-diesel oil.

Higher cyclic variability in combustion adversely influences emissions, efficiency, and driveability of internal combustion engines. In this paper, we used wavelet transform techniques to investigate the dynamical characteristics of a combustion process in numerous combustion parameters of a 4-cylinder turbocharged common rail direct injection (CRDI) diesel engine fuelled with Argemone mexicana biodiesel (AGB)/diesel blended fuel. In addition, statistical analysis is described to validate the results of the wavelet spectrum methods for cyclic variation in the diesel engine. The results show that the cyclic variations in IMEP and Pmax are sensitive to the engine load and fuel properties. The coefficient of variation of both combustion parameters decreases as engine load increases for all tested fuels. Moreover, adding Argemone mexicana biodiesel (AGB) into diesel fuel up to 20% (AB20) reduces cyclic variations in combustion parameters at all tested engine loads. Furthermore, the global wavelet spectrum and wavelet power spectrum are utilized to identify the dominant oscillatory combustion modes. The cycle-to-cycle fluctuations in combustion parameters (i.e., IMEP and Pmax) exhibit multi-scale dynamics for all experimental conditions. Compared to long and intermediate oscillations in diesel fuel, AB10 and AB20 fuel showed short and intermittent period fluctuations. The findings of this experimental work will be helpful to optimize engine control strategies for AGB/diesel blended fueled multi-cylinder CRDI diesel engines.

The impact of the first galaxies on cosmic dawn and reionization

ABSTRACT The formation of the first galaxies during cosmic dawn and reionization (at redshifts z = 5–30), triggered the last major phase transition of our universe, as hydrogen evolved from cold and neutral to hot and ionized. The 21-cm line of neutral hydrogen will soon allow us to map these cosmic milestones and study the galaxies that drove them. To aid in interpreting these observations, we upgrade the publicly available code 21cmFAST. We introduce a new, flexible parametrization of the additive feedback from: an inhomogeneous, H2-dissociating (Lyman–Werner; LW) background; and dark matter – baryon relative velocities; which recovers results from recent, small-scale hydrodynamical simulations with both effects. We perform a large, ‘best-guess’ simulation as the 2021 installment of the Evolution of 21-cm Structure (EOS) project. This improves the previous release with a galaxy model that reproduces the observed UV luminosity functions (UVLFs), and by including a population of molecular-cooling galaxies. The resulting 21-cm global signal and power spectrum are significantly weaker, primarily due to a more rapid evolution of the star formation rate density required to match the UVLFs. Nevertheless, we forecast high signal-to-noise detections for both HERA and the SKA. We demonstrate how the stellar-to-halo mass relation of the unseen, first galaxies can be inferred from the 21-cm evolution. Finally, we show that the spatial modulation of X-ray heating due to relative velocities provides a unique acoustic signature that is detectable at z ≈ 10–15 in our fiducial model. Ours are the first public simulations with joint inhomogeneous LW and relative-velocity feedback across the entire cosmic dawn and reionization, and we make them available at this link https://scholar.harvard.edu/julianbmunoz/eos-21.

Large-scale climate variability footprint in water levels of alluvial aquifers across Iran

The ability to predict the future variability of groundwater resources in time and space is of critical importance in society’s adaptation to climate variability and change. Periodic control of large-scale ocean-atmospheric circulations on groundwater levels serves as a potentially effective source for relatively long-term forecasting. In this study, as a first national-scale assessment, we use the continuous wavelet transform, global power spectrum, and wavelet coherence analyses to quantify the linkage between the Atlantic Multidecadal Oscillation (AMO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), and El Niño Southern Oscillation (ENSO) and the representative groundwater levels of the 24 principal aquifers, scattered across 14 different climate zones of Iran. Aquifer storage variations are found to be partially derived from annual to interdecadal climate variability and not solely a function of pumping variability. Moreover, teleconnections are observed to be both frequency and time-specific. The significant coherence patterns between the climate indices and groundwater levels are concentrated at five frequency bands of the annual (~ 1 year), short-interannual (2–4 years), medium-interannual (4–6 years), decadal (8–12 years), and interdecadal (14–18 years), consistent with the dominant oscillations of climate indices. AMO’s strong linkage to groundwater variability is found to be at interdecadal and annual modes of groundwater levels while PDO’s highest imprint is concentrated in interannual, decadal, and interdecadal periodicities. Unlike ENSO for which the highest modulating influence is found to be across the decadal and interannual modes, the NAO’s footprint in aquifers is marked at annual and interdecadal frequency bands. Findings further show that the groundwater variability is driven primarily by a combination of multiple large-scale atmospheric circulations rather than a single atmospheric circulation index. The decadal and interdecadal oscillation modes are the dominant modes in Iranian aquifers. Findings also mark the unsaturated zone contribution in damping and lagging of the climate variability modes, particularly for the higher-frequency indices of ENSO and NAO so that groundwater variability is more correlated with those climate circulations that have lower frequency such as PDO and AMO. Finally, the data length is found to have a significant effect on the teleconnections if the time series are not contemporaneous and for each particular series, only one value of coherence/correlation is computed instead of separate computations for different frequency bands and different time spans.

Identification of precipitation zones in Pakistan by Global Wavelet Power Spectrum

Identification of precipitation zones in Pakistan by Global Wavelet Power Spectrum

Identification of precipitation zones in Pakistan by global wavelet power spectrum

Identification of precipitation zones in Pakistan by global wavelet power spectrum

Does Quantitative Electroencephalography Refine Preoperative Cognitive Assessment in Parkinson’s Disease Patients Treated with Deep Brain Stimulation? A Follow-Up Study

Objective: Deep brain stimulation (DBS) in Parkinson’s disease (PD) is associated with an increased risk of post-operative cognitive deterioration. Preoperative neuropsychological testing can be affected and limited by the patient’s collaboration in advanced disease. The purpose of this study was to determine whether preoperative quantitative electroencephalography (qEEG) may be a useful complementary examination technique during preoperative assessment to predict cognitive changes in PD patients treated with DBS. Methods: We compared the cognitive performance of 16 PD patients who underwent bilateral subthalamic nucleus DBS to the performance of 15 PD controls (matched for age, sex, and education) at baseline and at 24 months. Cognitive scores were calculated for all patients across 5 domains. A preoperative 256-channel resting EEG was recorded from each patient. We computed the global relative power spectra. Correlation and linear regression models were used to assess associations of preoperative EEG measures with post-operative cognitive scores. Results: Slow waves (relative delta and theta band power) were negatively correlated with post-operative cognitive performance, while faster waves (alpha 1) were strongly positively correlated with the same scores (the overall cognitive score, attention, and executive function). Linear models revealed an association of delta power with the overall cognitive score (p = 0.00409, adjusted R² = 0.6341). Verbal fluency (VF) showed a significant decline after DBS surgery, which was correlated with qEEG measures. Conclusions: To analyse the side effects after DBS in PD patients, the most important parameter is verbal fluency capacity. In addition, correlation with EEG frequency bands might be useful to detect particularly vulnerable patients for cognitive impairment and be supportive in the selection process of patients considered for DBS.

Time series analysis of observed maximum and minimum air temperature at four urban cities of India during 1951-2015

A sixty - five year (1951-2015) long data for monthly minimum temperature (TMIN) and maximum temperature (TMAX), observed by the India Meteorological Department (IMD), is statistically analyzed at four urban stations namely Bhubaneswar, Delhi, Mumbai and Chennai of India. A bimodal nature in seasonality is noticed for TMAX and TMIN at all locations. Two peaks for TMAX and TMIN are observed in May and September. Exceptionally, Mumbai shows TMAX peaks during May and November and Delhi shows TMIN peaks during June and September. Higher standard deviations (SD) for TMAX is noted at Delhi with a maximum in March (1.78 °C), while for Chennai, the SD for TMIN is lesser compared to other cities. Two different periods 1951-1980 (P1, the first half of the study period) and 1981-2015 (P2, the second half of the study period) were identified from the time series of both TMAX and TMIN. A higher increasing trend is observed during P2 than P1 in all the cities except in TMIN at Mumbai. The highest increasing trend (0.040 °C/year) is observed for TMIN in Mumbai during P1 time, but the trend is almost constant (0.001 °C/year) during P2 time. The highest increasing trend for TMIN at Mumbai is mainly contributed by the increasing trend in post-monsoon and winter months in P1. Surprisingly, in both P1 and P2, the trends are less during monsoon months for all the cities. A consistent 5-year (3-year) band is observed throughout the wavelet power spectrum at the coastal cities Bhubaneswar, Mumbai (Chennai). However, the 5-year signal is not consistent at Delhi and it is observed only during the year 1975-1980. The global wavelet power spectrum showed that TMIN at Chennai has less power (0.6 °C2) corresponding to 3-year signal and Mumbai has highest power (12 °C2) corresponding to the 5-year signal in comparison to other cities.

P 37. The cognitive course in Parkinson's disease patients treated with Deep Brain Stimulation of subthalamic nucleus – Correlations with qEEG

Objective. Deep brain stimulation of the subthalamic nucleus (STN-DBS) improve motor functioning in Parkinson“s disease patients– Additionally it can also influence cognitive functioning. In most patients, a decline of verbal fluency test performance after STN-DBS is often reported. The purpose of this study was to determine whether preoperative quantitative electroencephalography (qEEG) may be useful in the preoperative assessment predicting cognitive changes in PD patients treated with STN-DBS. Methods. We compared the cognitive performance of 16 PD patients who underwent bilateral STN-DBS and 15 PD controls (matched for age, sex and education) at baseline as well as after 24 months. Cognitive scores were calculated for all patients across 5 cognitive domains covered by different cognitive tests (attention, executive function, episodic & working memory, verbal fluency, visuo-construction). A preoperative 256-channels EEG was recorded of each patient at rest and global relative power spectra were computed. Correlation and linear regression models were used to assess the association of preoperative EEG measures with postoperative cognitive scores in stimulated PD patients compared to PD controls. Results. The decline of verbal fluency performance after two years in the PD group treated with STN-DBS was significant in comparison to the PD controls ( p < 0.001). All other cognitive domains declined in the same manner as in the PD controls or remained stable over the 24-months period. EEG frequency analyses revealed increased global theta power (4-8 Hz) at baseline to be associated with reduced postoperative verbal fluency performance ( r = 0.50, p < 0.05) at follow-up. Conclusion. Consistent with the results of former studies, STN stimulation deteriorates verbal fluency in the course of two years. Accordingly, we observed significant decline in tests measuring verbal fluency in the group of PD patients treated with STN-DBS compared to a control group of PD patients. The decline of verbal fluency performance could be predicted by a reduced theta power at baseline. No other significant differences in cognitive alterations between patients in the STN-DBS group and the PD control group occur over two years.

Multiscale Spatiotemporal Analysis of Extreme Events in the Gomati River Basin, India

Accelerating climate change is causing considerable changes in extreme events, leading to immense socioeconomic loss of life and property. In this study, we investigate the characteristics of extreme climate events at a regional scale to -understand these events’ propagation in the near future. We have considered sixteen extreme climate indices defined by the World Meteorological Organization’s Expert Team on Climate Change Detection and Indices from a long-term dataset (1951–2018) of 53 locations in Gomati River Basin, North India. We computed the present and future spatial variation of theses indices using the Sen’s slope estimator and Hurst exponent analysis. The periodicities and non-stationary features were estimated using the continuous wavelet transform. Bivariate copulas were fitted to estimate the joint probabilities and return periods for certain combinations of indices. The study results show different variation in the patterns of the extreme climate indices: D95P, R95TOT, RX5D, and RX showed negative trends for all stations over the basin. The number of dry days (DD) showed positive trends over the basin at 36 stations out of those 17 stations are statistically significant. A sustainable decreasing trend is observed for D95P at all stations, indicating a reduction in precipitation in the future. DD exhibits a sustainable decreasing trend at almost all the stations over the basin barring a few exceptions highlight that the basin is turning drier. The wavelet power spectrum for D95P showed significant power distributed across the 2–16-year bands, and the two-year period was dominant in the global power spectrum around 1970–1990. One interesting finding is that a dominant two-year period in D95P has changed to the four years after 1984 and remains in the past two decades. The joint return period’s resulting values are more significant than values resulting from univariate analysis (R95TOT with 44% and RTWD of 1450 mm). The difference in values highlights that ignoring the mutual dependence can lead to an underestimation of extremes.

Snowmelt characteristics in a pristine mountain catchment of the Jalovecký Creek, Slovakia, over the last three decades

AbstractThe Jalovecký Creek catchment, Slovakia (area 22.2 km2, mean elevation 1500 m a.s.l.), is likely the last big valley complex in the Carpathian Mountains, in which the hydrological cycle is still governed by natural processes. Hydrological research is conducted there since the end of the 1980s. The overall mission of the research is to increase the knowledge about the hydrological cycle in the highest part of the Carpathians. The research agenda, briefly introduced in the first part of this article, is focused on water balance, snow accumulation and melt and runoff formation. Recent analysis of precipitation, discharge, snow cover and isotopic data from period 1989–2018 indicates that hydrological cycle has become more dynamic since 2014. Although several indicators suggest that it could be related to the cold part of the year, direct links with snow storage and the contribution of snowmelt water to catchment runoff were not confirmed. The second part of the article is therefore focused on an analysis of daily cycles in streamflow in March to June 1988–2018 to obtain a deeper insight into the snowmelt process. We describe characteristics of the cycles and examine their variability over the study period. The results indicate that less snow at the lowest elevations (800–1150 m a.s.l.) since 2009 could have influenced the cessation of the cycles in June since 2010. The possible role of the decreased amount of snow at the lowest elevations in changes in runoff characteristics is also suggested by an increase in time lags between maximum discharges during the events and maximum air temperatures preceding discharge maxima measured near the catchment outlet (at 750 m a.s.l.) in spring 2018 compared to springs with a similar number of streamflow cycles in the years 1988, 2000 and 2009. Wavelet analysis did not indicate changes in global power spectra in hourly discharge and air temperature data.

Beta Electroencephalographic Oscillation Is a Potential GABAergic Biomarker of Chronic Peripheral Neuropathic Pain.

This preliminary investigation aimed to assess beta (β) oscillation, a marker of the brain GABAergic signaling, as a potential objective pain marker, hence contributing at the same time to the mechanistic approach of pain management. This case–control observational study measured β electroencephalographic (EEG) oscillation in 12 right-handed adult male with chronic neuropathic pain and 10 matched controls (∼55 years). Participants were submitted to clinical evaluation (pain visual analog scale, Hospital Anxiety, and Depression scale) and a 24-min high-density EEG recording (BIOSEMI). Data were analyzed using the EEGlab toolbox (MATLAB), SPSS, and R. The global power spectrum computed within the low (Lβ, 13–20 Hz) and the high (Hβ, 20–30 Hz) β frequency sub-bands was significantly lower in patients than in controls, and accordingly, Lβ was negatively correlated to the pain visual analog scale (R = −0.931, p = 0.007), whereas Hβ correlation was at the edge of significance (R = −0.805; p = 0.053). Patients’ anxiety was correlated to pain intensity (R = 0.755; p = 0.003). Normalization of the low and high β global power spectrum (GPS) to the GPS of the full frequency range, while confirming the significant Lβ power decrease in chronic neuropathic pain patients, vanished the significance of the Hβ decrease, as well as the correlation between Lβ power and pain intensity. Our results suggest that the GABAergic Lβ EEG oscillation is affected by chronic neuropathic pain. Confirming the Lβ GPS decrease and the correlation with pain intensity in larger studies would open new opportunities for the clinical application of gamma-aminobutyric acid-modifying therapies.

Decreased resting-state alpha-band activation and functional connectivity after sleep deprivation

Cognitive abilities are impaired by sleep deprivation and can be recovered when sufficient sleep is obtained. Changes in alpha-band oscillations are considered to be closely related to sleep deprivation. In this study, power spectrum, source localization and functional connectivity analyses were used to investigate the changes in resting-state alpha-band activity after normal sleep, sleep deprivation and recovery sleep. The results showed that the global alpha power spectrum decreased and source activation was notably reduced in the precuneus, posterior cingulate cortex, cingulate gyrus, and paracentral lobule after sleep deprivation. Functional connectivity analysis after sleep deprivation showed a weakened functional connectivity pattern in a widespread network with the precuneus and posterior cingulate cortex as the key nodes. Furthermore, the changes caused by sleep deprivation were reversed to a certain extent but not significantly after one night of sleep recovery, which may be due to inadequate time for recovery sleep. In conclusion, large-scale resting-state alpha-band activation and functional connectivity were weakened after sleep deprivation, and the inhibition of default mode network function with the precuneus and posterior cingulate cortex as the pivotal nodes may be an important cause of cognitive impairment. These findings provide new insight into the physiological response to sleep deprivation and determine how sleep deprivation disrupts brain alpha-band oscillations.

Quasi-periodic Pulsation Detected in Lyα Emission During Solar Flares

Abstract We investigated the quasi-periodic pulsation (QPP) in Lyα, X-ray, and extreme-ultraviolet (EUV) emissions during two solar flares, i.e., an X-class (SOL2012-01-27T) and a C-class (SOL2016-02-08T). The full-disk Lyα and X-ray flux during these solar flares were recorded by the EUV Sensor and X-Ray Sensor on board the Geostationary Operational Environmental Satellite. The flare regions were located from the EUV images measured by the Atmospheric Imaging Assembly. The QPP could be identified as a series of regular and periodic peaks in the light curves, and its quasi-periodicity was determined from the global wavelet and Fourier power spectra. A quasi-periodicity at about 3 minutes is detected during the impulsive phase of the X-class flare, which could be explained as the acoustic wave in the chromosphere. Interestingly, a quasi-periodicity at roughly 1 minute is discovered during the entire evolutionary phases of solar flares, including the precursor, impulsive, and gradual phases. This is the first report of 1 minute QPP in the Lyα emission during solar flares, in particular during the flare precursor. It may be interpreted as a self-oscillatory regime of the magnetic reconnection, such as magnetic dripping.