Peak Frequency Research Articles

Investigating noise radiation pattern of railway bridges for urban rail transit

Noise from elevated railway bridges in urban areas has been causing severe environmental issues. To find the peak frequency of sound energy and thus develop targeted countermeasures for noise mitigation, policymakers need a better understanding of the noise characteristics of urban railway bridges during train passage. This study approaches this issue by combining transient dynamic analyses and acoustic finite element simulation. Our frequency characteristic analysis focused on two primary noise sources: wheel-rail noise and bridge-borne noise. We built a spatial noise model that predicts the radiated noise of urban railway bridges in both the active and planning stages. The results show that the wheel-rail noise peaked at 800 Hz, 1600 Hz, and 3150 Hz, while the bridge-borne noise peaked at 40 Hz, 50 Hz, 80 Hz, and 125 Hz. The validation results demonstrated that the developed noise spatial prediction model extended well to similar urban railway bridges.

Dynamic auditory-acoustic properties differentiate word-final fricatives in Gitksan

Gitksan is an endangered, understudied Tsimshian language spoken in northwestern British Columbia. This study aims to provide an acoustic-phonetic description of Gitksan fricatives. Isolated words containing word-final fricatives were recorded with three Gitksan first-language speakers (2 male, 1 female; age 65 + ). Postvocalic fricative tokens ([s] = 159, [ɬ] = 123, [ç] = 180, [Xw] = 150, [χ] = 267) were analyzed. Dynamic, spectral measurements of peak frequency in equivalent rectangular bandwidths (peakERB; Reidy, 2016) were estimated from 17 20-ms windows centered at equidistant points between the onset and offset of the fricative. The resulting trajectories (windows 3–15) show that the peakERB decreases as the place of articulation shifts further back in the vocal tract (alveolar [s]∼25–31, palatal [ç]∼20–23, uvular [χ]∼17–19; exception: labiovelar [Xw]∼15–16, likely lowered by labialization). Also, the lateral alveolar [ɬ] shows a lower peakERB (∼22–24) than the central alveolar [s]. A random forest model (Breiman, 2001) was trained on 586 tokens in predicting the fricatives with peakERB measures of the 17 windows. The trained model was tested on 293 tokens; it identified [s] most accurately and [ɬ] least accurately. Additionally, the model's relative-importance ranking of the 17 peakERB predictors suggests that the middle third of the peakERB trajectory is robust in differentiating word-final fricatives in Gitksan.

Modelling and numerical simulation of a concentrated mass-based branch vibration

To investigate the variation patterns of spectral characteristics of main branches in fruit trees due to uneven distribution of growth, pruning, and fruit quality, as well as to determine the optimal fruit vibration harvesting, a vibration differential model of tree branches was constructed using the concentrated mass method. A three-dimensional model of the fruit tree was built using finite element software. The spectral characteristics of each branch were obtained using the linear sweep frequency method. The method of changing the mass of each branch was employed to simulate the variation in branch mass. The frequency of occurrence, mean acceleration, and coefficient of variation of the inherent frequencies were used as evaluation indicators. The results revealed that when the vibration frequency of the fruit tree was at the maximum peak frequency in the tree trunk spectrum, the frequency of occurrence and mean acceleration were optimal. And the coefficient of variation of branch acceleration was less than 1, indicating a moderate dispersion. Additionally, reducing the branch mass resulted in an increase in the maximum peak frequency of the tree trunk spectrum. Therefore, the growth, pruning, and uneven distribution of the branches in fruit trees can alter the spectral characteristics. When harvesting fruit by vibration, selecting the maximum peak frequency in the trunk vibration spectrum as the excitation force frequency yields the best fruit detachment.

Effects of the Northern Hemisphere sudden stratospheric warmings on the Sporadic-E layers in the Brazilian sector

Tidal and Planetary Wave (PWs) amplitudes are strongly influenced by Sudden Stratospheric Warming (SSW) events. A nonlinear interaction between the tidal winds and planetary waves during the SSW may contribute to the intensification of sporadic-E (Es) layers in the lower thermosphere. This work investigated the relationship between SSW events in the Northern Hemisphere and the Es layer occurrence at low latitudes in the Brazilian sector. We used data from digital ionosondes installed in the observatories of Araguatins (ARA, 5.65° S; 48.12° W; dip lat. −5.44°) and São José dos Campos (SJC, 23.18° S; 45.89° W; dip lat. −21.37°) to analyze the Es layers. Additionally, we used the temperature, zonal wind, and PWs data at high latitudes in the Northern Hemisphere during the major SSW event that occurred in February/2018 and during the events of Dec/2018–Jan/2019 and Dec/2020–Jan/2021. The results showed a maximum frequency peak of 20 MHz (∼5 × 106 electrons.cm−3) at ARA and SJC during these SSW events. The large values of ftEs, fbEs, and electronic densities were observed between 100 and 115 km height in the Esf/l type layers during daytime or nighttime periods. The results also showed that the number of large values of ftEs, fbEs, and electronic density of the Es layer was much higher in ARA than in SJC, in general. The wavelet power spectrum analyses of the ftEs and fbEs showed a periodicity of 2-days before and after the central day of the SSWs events at the station of ARA, with three prominent peaks in the 2018/2019 event. At the SJC station the quasi-2-day periodicity in the wavelet analyses of the ftEs was observed after the central day in all three SSW events, with a peak before the central day during the 2020/2021 event.

Machine learning for analysis of wind farm noise

Two challenges associated with analyzing acoustical data from wind farms are: 1) separating turbine sounds from environmental sounds and 2) classifying acoustical samples into different types of wind turbine noise based on acoustic characteristics. Machine-learning methods for classifying general environmental sounds have been developed using large human classified databases (e.g., YAMNet), but only a few studies have targeted classification of wind farm noise (WFN) specifically. Techniques for classifying wind farm noise have focused on identification of amplitude modulation (AM) using both traditional methods such as low frequency peak prominence (IOA method) and machine learning methods using both targeted AM acoustics features and more general deep acoustic features. To address these two challenges, we are developing a multi-echelon machine learning framework to identify and classify noise from wind farms using publicly available windfarm data and open-source software. The first echelon provides an automated method for identifying WFN samples that are free of environmental sounds. The second echelon uses machine learning to classify these wind farm noises according to the degree of AM, prominent tones, and other factors that might contribute to the human response and are incorporated in the metrics used or potentially used to assess compliance with regulations.

Study on fracture characteristics and fracture mechanism of fully recycled aggregate concrete using AE and DIC techniques

Fracture behaviors of manufactured sand-recycled coarse aggregate concrete (MSRCAC), recycled fine aggregate concrete (RFAC), and fully recycled aggregate concrete (FRAC) were investigated by performing three-point bending tests on single-edge notched beams using acoustic emission (AE) and digital image correlation (DIC) techniques. The effects of recycled fine aggregate (RFA), recycled coarse aggregate (RCA), and their interaction on fracture parameters, AE characteristics and fracture process zone (FPZ) of concrete were comparatively analyzed. The results indicated that it was mainly transgranular failure and intergranular failure for RFAC, while transgranular failure for MSRCAC and FRAC. The RFA and RCA reduced the initial fracture toughness, unstable fracture toughness and fracture energy of concrete, and their interaction resulted in poor fracture properties of FRAC. In addition, the RFA and RCA significantly increased the AE activities, the proportion of tensile cracks, and the high-frequency amplitude and peak frequency of concrete. The toughening effect of FPZ was weakened by the RFA and RCA, resulting in an obvious increase in FPZ length and damage propagation of FRAC. Meanwhile, the width of FPZ was reduced, the maximum FPZ width of concrete with different combinations of recycled aggregates was approximately 1.8–3.1 times the maximum aggregate size, which could be explained as the RFA and RCA reduced the aggregate interlocking effect of concrete, while the RCA had a greater effect on the damage mechanism and FPZ of concrete than the RFA.

The Remarkable Predictive Power of Infrared Data of Blazars

Blazars are the brightest and most abundant persistent sources in the extragalactic γ-ray sky. Due to their significance, they are often observed across various energy bands, where the data of which can be used to explore potential correlations between emission at different energies, yielding valuable insights into the emission processes of their powerful jets. In this study we utilized IR data at 3.4 and 4.6 μm from the Near-Earth Object Wide-field Infrared Survey Explorer Reactivation Mission, spanning 8 yr of observations, X-ray data from the Neil Gehrels Swift Observatory collected throughout the satellite’s lifetime, and 12 years of γ-ray measurements from the Fermi Large Area Telescope’s all-sky survey. Our analysis reveals that the IR spectral slope reliably predicts the peak frequency and maximum intensity of the synchrotron component of blazar spectral energy distributions, provided it is uncontaminated by radiation unrelated to the jet. A notable correlation between the IR and γ-ray fluxes was observed, with the BL Lacertae subclass of blazars displaying a strong correlation coefficient of r = 0.80. IR band variability is more pronounced in flat spectrum radio quasars than in BL Lacertae objects, with mean fractional variability values of 0.65 and 0.35, respectively. We also observed that the synchrotron peak intensity of intermediate-high-energy-peaked objects can forecast their detectability at very high γ-ray energies. We used this predicting power to identify objects in current catalogs that could meet the detection threshold of the Cerenkov Telescope Array extragalactic survey, which should encompass approximately 180 blazars.

Absorption dispersion below boson peak frequency in oxide glasses studied by THz-time domain spectroscopy

The dispersion properties of the absorption coefficients [α(ν)] of different multi-component silicate oxide glasses have been studied in the frequency region below the boson peak by using THz-time-domain spectroscopy. The value of α(ν)/ν2 has been shown to exhibit a minimum level (R) at low frequency and subsequently a linear increase in the form of r(ν/νBP + E) with increasing frequency, where νBP is the boson peak frequency and R, r, and E are material-specific constants. It has also been found that R ∝ r and E is a constant common to most glasses. This α(ν)/ν2 behavior is ascribed to the dispersion property of the light-vibration coupling coefficient under the reasonable vibrational density of state function. The minimum (constant) and linear terms of the α(ν)/ν2 spectrum are originated from the physical/chemical disorder-induced charge fluctuations in the long-range scale (constant term) and short-range scale (linear term), respectively. The fluctuating charge (σ1) caused by uncorrelated, long-range disorders has primary significance for determining the sub-THz absorption dispersion properties, and its value has been determined for each glass material.

A Study of Broad Emission Line and Doppler Factor Estimation for Fermi Blazars

In this work, we obtained a sample of 979 Fermi blazars with broad emission lines, including 701 objects collected from published works and 278 objects developed in this work. For the 278 objects, we made a crossmatch from three catalogs, the Fermi Large Area Telescope Fourth Source Catalog (4FGL), the Sloan Digital Sky Survey, and the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, and calculated the broad-line region (BLR) luminosity. Then, we estimated the Doppler factor and studied the correlations between the BLR luminosities and the γ-ray luminosities, the synchrotron peak frequency (ν p ), and Doppler factor (δ) for the whole sample. Our analyses and discussions came to the following main conclusions: For the 278 blazars, their BLR luminosity (log L BLR) ranges from 40.44 to 45.45 erg s−1, with a mean value of 43.39 erg s−1. The Doppler factor ranges from δ = 0.45 to δ = 88.52, with a mean value of 12.99 for the 979 Fermi blazars, which is consistent with the results in the literature. Both the BLR luminosity and the Doppler factor exhibit positive correlations with the γ-ray luminosity. The BLR luminosity is anticorrelated with synchrotron peak frequency, implying a Compton cooling. A line of logLBLR=1.58logνp−19.46 separating BL Lacertae objects and flat-spectrum radio quasars was obtained in the diagram of logLBLR against logνp using a machine-learning method. Based on the analysis of the equivalent width and the Doppler factors, we proposed five changing-look blazar candidates.

Moisture Effects on Acoustic Emission Characteristics and Damage Mechanisms of Balsa Wood Core Composite Sandwich under 4-Point Bending.

To contribute to the development of sustainable composites, this work investigates the effects of moisture on the key AE characteristics related to the damage mechanisms of a bio-based balsa wood core sandwich in 4-point bending tests, including cumulative counts, amplitude, peak frequency, and duration. Novel triple dog-bone balsa wood core sandwich specimens with different MC (moisture content) were studied by comparing microscopic observations and a proposed two-step clustering approach in AE analysis. Three MC states, i.e., dry, 50% MC, and 120% MC, are discussed. GFRP (glass-fiber-reinforced polymer) laminate skin damages were found to be predominant in most GFRP-balsa sandwich specimens, but balsa wood core damages play a more important role as MC increases. The degradation of the bending stiffness of the sandwich was proven to be faster in the first linear stage of the moisture absorption curve, while the decrease in bending strength was more pronounced at the MC saturation level. Finally, for all of the dry and wet sandwich specimens, peak frequency and duration were proven to be more helpful in identifying damages associated with the lighter bio-based balsa wood core, such as balsa core damages and skin/core debonding.

Chronic oxytocin improves neural decoupling at rest in children with autism: an exploratory RCT.

Shifts in peak frequencies of oscillatory neural rhythms are put forward as a principal mechanism by which cross-frequency coupling/decoupling is implemented in the brain. During active neural processing, functional integration is facilitated through transitory formations of "harmonic" cross-frequency couplings, whereas "nonharmonic" decoupling among neural oscillatory rhythms is postulated to characterize the resting, default state of the brain, minimizing the occurrence of spurious, noisy, background couplings. Within this exploratory, randomized, placebo-controlled trial, we assessed whether the transient occurrence of nonharmonic and harmonic relationships between peak-frequencies in the alpha (8-14 Hz) and theta (4-8 Hz) bands is impacted by intranasal administration of oxytocin, a neuromodulator implicated in improving homeostasis and reducing stress/anxiety. To do so, resting-state electroencephalography was acquired before and after 4 weeks of oxytocin administration (12 IU twice-daily) in children with autism spectrum disorder (8-12 years, n = 33 oxytocin; n = 34 placebo). At the baseline, neural assessments of children with autism were compared with those of a matched cohort of children without autism (n = 40). Compared to nonautistic peers, autistic children displayed a lower incidence of nonharmonic alpha-theta cross-frequency decoupling, indicating a higher incidence of spurious "noisy" coupling in their resting brain (p = .001). Dimensionally, increased neural coupling was associated with more social difficulties (p = .002) and lower activity of the parasympathetic "rest & digest" branch of the autonomic nervous system (p = .018), indexed with high-frequency heart-rate-variability. Notably, after oxytocin administration, the transient formation of nonharmonic cross-frequency configurations was increased in the cohort of autistic children (p < .001), indicating a beneficial effect of oxytocin on reducing spurious cross-frequency-interactions. Furthermore, parallel epigenetics changes of the oxytocin receptor gene indicated that the neural effects were likely mediated by changes in endogenous oxytocinergic signaling (p = .006). Chronic oxytocin induced important homeostatic changes in the resting-state intrinsic neural frequency architecture, reflective of reduced noisy oscillatory couplings and improved signal-to-noise properties.

Transfer learning model for estimating site amplification factors from limited microtremor H/V spectral ratios

Summary Site amplification factors (SAFs) of seismic ground motions are essential in evaluating and estimating seismic hazards. In our previous study, the authors proposed a simple and cost-effective method to estimate a SAF based on a deep neural network (DNN) model and microtremor horizontal-to-vertical spectral ratio (MHVR). Since the previous DNN model was based on the observed SAFs and MHVRs within a limited district in Japan, the applicability of the previous model to non-source regions with different site conditions was limited. This study explored the application of a transfer learning (TL) technique to adapt an existing (pre-trained) DNN model to new regions and a different database. The SAFs obtained through generalized spectral inversion technique (GIT) at the seismic observation stations (K-NET and KiK-net) in Japan were collated as the ground truth for site effects. MHVRs recorded at the stations in several districts of Japan were collected to construct a dataset for the development of the TL model. Subsequently, a TL model was constructed, leveraging the neural network layers and their weights from the pre-trained model while incorporating additional neural network layers to enhance the performance. During the training process, a total data set of 112 sites was divided into training set, validation set, and external test set by 1:1:5. Utilizing a cross-validation strategy, the residuals between pSAFs (pseudo-SAFs) estimated by the TL model and the observed SAFs were analyzed for the external test set containing 80 sites. The results showed that the TL model outperformed the pre-trained DNN model. The cross-validation results demonstrated that almost consistent prediction results were obtained regardless of any combination of 16 sites selected as the training set. Furthermore, by contrasting the influence of varying training set sizes on the performance of the TL model and comparing the TL model to a DNN model with an extended training set, the effectiveness of constructing the model with the limited number of data (16 sites) was ascertained. Finally, the effectiveness and limitations of the TL model were evaluated using MHVRs with peak frequencies falling outside the training set's range.

Dynamic modeling and experimental study of multi-coil composite core energy harvester

To improve the output performance and working bandwidth of the magnetic levitation energy harvester, a new structure of multi-coil composite core energy harvester was proposed and verified by numerical simulation and experiment. Firstly, the working mechanism of the energy harvester was analyzed. The dynamics model and COMSOL Multiphysics finite element analysis model were established to study the influence of magnetic field and coil position on the output characteristics of the energy harvester. Then, an experimental platform was built to verify the proposed new energy harvester, and the influence of coil position on the output power of the energy harvester was explored. Three kinds of coil thicknesses, two kinds of coil winding directions, six kinds of coil connection modes and two kinds of magnet distribution experiments were designed. Finally, the influence of suspended core mass on output performance was analyzed, and the energy harvester with or without magnetic liquid was tested by sweeping frequency to evaluate its output performance. The experimental results show that the magnetic field near the coil can be enhanced effectively, and the output performance of the whole system can be improved by the magnet mutual repulsion energy harvester with coil connection mode CM1 in a consistent coil direction. With the increase of composite core mass, the output voltage and power of the energy harvester also increase, but the peak frequency decreases. Compared with the energy harvester without magnetic liquid, the output power performance of the designed multi-coil composite core energy harvester is significantly improved by 29%, and the frequency bandwidth growth rate is 73%. The normalized power density can reach 134.26 μWcm−3g−2. The new structure of the multi-coil composite core provides important reference significance for designing a more efficient magnetic levitation energy harvester.

Relationship between acoustic traits of protesting cries of domestic kittens (Felis catus) and their individual chances for survival

Domestic cat (Felis catus) mothers may rely on offspring cries to allocate resources in use of individuals with greater chances for survival and sacrifice the weak ones in case of impossibility to raise the entire large litter. Potential victims of this maternal strategy can enhance their chances of survival, by producing vocalizations with traits mimicking those of higher-quality offspring. We compared acoustic traits of 4990 cries produced during blood sampling by 57 two-week-old captive feral kittens (28 males, 29 females); 47 of them survived to 90 days of age and 10 died by reasons not related to traumas or aggression. No relationship was found between acoustic parameters and kitten survival, however, positive relationship was found between survival and body weight. The cries had moderate cues to individuality and lacked cues to sex. Body weight correlated positively with fundamental frequency and negatively with call rate, duration, peak frequency and power quartiles. We discuss that dishonesty of acoustic traits of kitten quality could develop as adaptation for misleading a mother from allocation resources between the weaker and stronger individuals, thus enhancing individual chances for survival for the weaker littermates. Physical constraint, as body weight, may prevent extensive developing the deceptive vocal traits.

Size and shapes effect on structural and phonon density of state in Ir nanoparticle and mechanical properties of Ir metal

Density Functional Theory (DFT) calculations were performed to assess the mechanical properties of an Ir metal nanocluster. The properties under investigation encompassed the bulk modulus, Poisson ratio, elastic shear modulus, and binding energy. The stability of the nanocluster was explored across various shapes, including icosahedron, octahedron, and truncated octahedron, with consideration given to different cluster sizes. The DFT results unveiled a general mechanism for amorphization in the Ir nanocluster of size 55, involving rosette-like structural transformations at the fivefold vertex. Additionally, Phonon Density of States (PDOS) was computed for the core, surface, and total atoms of Ir nanoclusters with sizes 38, 55, and 75, as well as for the Ir bulk structure. These calculations were carried out using both Quantum Sutton-Chen potential (QSC) and Gupta potential models through molecular dynamics (MD) methods. The analysis of the phonon density of states revealed that the contributions of two main peaks in low and high frequencies were associated with the surface and core atoms, respectively. Furthermore, the extension of the phonon density of states was performed for larger-sized Ir nanoclusters. Significantly, the QSC potential was utilized to investigate the adsorption of main peaks and their trends for the phonon density of states in the Ir bulk structure. The results obtained from this method were found to be in agreement with experimental data derived from cold neutron and multidetector time-of-flight spectrometer phonon measurements.

Feasibility study of a method for tuning wave energy converters

A Wave Energy Converter (WEC) cannot operate efficiently if left alone floating to the mercy of the random sea surface. For a WEC to be efficient, its natural frequency should be close to the peak frequency of the local dominant waves. This paper introduces a method for tuning any natural frequency of a WEC to the peak frequency of a wave spectrum and converting dynamic responses into mechanical power. An innovative lightweight and dual-drag force subsea structure with a huge amount of entrapped seawater can be attached to any vessel using cables. This structure increases the roll mass moment of inertia of a medium size barge, and the mass of a small heaving floater, reducing their natural frequencies. The tension in the cables is converted into mechanical power. The study focuses on the Galápagos Islands, where swells with a typical 13 s peak period and significant wave height between 1 to 3 m dominate the wave climate. For the dynamic systems based on roll and heave responses, annual averages of mechanical power are 178 and 73 kW with efficiencies of 49 and 67% respectively. The method developed in this paper can improve the efficiency of existing and novel WECs.

Dwarf minke whales (Balaenoptera acutorostrata) acoustic signals from the South Atlantic Ocean

At present, there are still populations of different balaenopterids that have never been acoustically recorded and observed simultaneously. In an opportunistic sighting of dwarf minke whales during winter in southeastern Brazil, we have registered six individuals and recorded over 200 acoustic signals. Signals were quantified and had their acoustic parameters extracted; the calling rate was estimated as the number of sounds per minute, and the repetition rate was estimated as the number of calls of the same type emitted per minute. Four call types were described: Ba1, Ba2, Ba3 and Ba4. The most common and distinctive call was the Ba1, composed of three components: one had a peak frequency of 615.0 ± 189.8 Hz, and the other had peak frequencies of 1632.0 ± 191.5 and 5038.2 ± 195.1 Hz. Ba1 repetition varied from 2.4 to 11.1 repetitions/min. Ba2 was the second most common call with a peak frequency of 485.2 ± 421.9 Hz. The total calling rate varied from 4.4 to 11.1 calls/min. The signals are in the same frequency range reported for other areas but contain different structures.

Single-channel qEEG characteristics distinguish delirium from no delirium, but not postoperative from non-postoperative delirium

ObjectiveThis exploratory study examined quantitative electroencephalography (qEEG) changes in delirium and the use of qEEG features to distinguish postoperative from non-postoperative delirium. MethodsThis project was part of the DeltaStudy, a cross-sectional,multicenterstudy in Intensive Care Units (ICUs) and non-ICU wards. Single-channel (Fp2-Pz) four-minutes resting-state EEG was analyzed in 456 patients. After calculating 98 qEEG features per epoch, random forest (RF) classification was used to analyze qEEG changes in delirium and to test whether postoperative and non-postoperative delirium could be distinguished. ResultsAn area under the receiver operatingcharacteristic curve (AUC) of 0.76 (95% Confidence Interval (CI) 0.71–0.80) was found when classifying delirium with a sensitivity of 0.77 and a specificity of 0.63 at the optimal operating point. The classification of postoperative versus non-postoperative delirium resulted in an AUC of 0.50 (95%CI 0.38–0.61). ConclusionsRF classification was able to discriminate delirium from no delirium with reasonable accuracy, while also identifying new delirium qEEG markers like autocorrelation and theta peak frequency. RF classification could not distinguish postoperative from non-postoperative delirium. SignificanceSingle-channel EEG differentiates between delirium and no delirium with reasonable accuracy. We found no distinct EEG profile for postoperative delirium, which may suggest that delirium is one entity, whether it develops postoperatively or not.

Resting EEG Periodic and Aperiodic Components Predict Cognitive Decline Over 10 Years.

Measures of intrinsic brain function at rest show promise as predictors of cognitive decline in humans, including EEG metrics such as individual α peak frequency (IAPF) and the aperiodic exponent, reflecting the strongest frequency of α oscillations and the relative balance of excitatory/inhibitory neural activity, respectively. Both IAPF and the aperiodic exponent decrease with age and have been associated with worse executive function and working memory. However, few studies have jointly examined their associations with cognitive function, and none have examined their association with longitudinal cognitive decline rather than cross-sectional impairment. In a preregistered secondary analysis of data from the longitudinal Midlife in the United States (MIDUS) study, we tested whether IAPF and aperiodic exponent measured at rest predict cognitive function (N = 235; age at EEG recording M = 55.10, SD = 10.71) over 10 years. The IAPF and the aperiodic exponent interacted to predict decline in overall cognitive ability, even after controlling for age, sex, education, and lag between data collection time points. Post hoc tests showed that "mismatched" IAPF and aperiodic exponents (e.g., higher exponent with lower IAPF) predicted greater cognitive decline compared to "matching" IAPF and aperiodic exponents (e.g., higher exponent with higher IAPF; lower IAPF with lower aperiodic exponent). These effects were largely driven by measures of executive function. Our findings provide the first evidence that IAPF and the aperiodic exponent are joint predictors of cognitive decline from midlife into old age and thus may offer a useful clinical tool for predicting cognitive risk in aging.

Gravitational wave footprints from Higgs-portal scalegenesis with multiple dark chiral scalars* *Supported in part by the National Science Foundation of China (NSFC) (11747308, 11975108, 12047569), the Seeds Funding of Jilin University (S.M.), and Toyama First Bank, Ltd (H.I.)

We discuss the gravitational wave (GW) spectra predicted from the electroweak scalegenesis of the Higgs portal type with a large number of dark chiral flavors, which many flavor QCD would underlie and give the dynamical explanation of the negative Higgs portal coupling required to trigger the electroweak symmetry breaking. We employ the linear-sigma model as the low-energy description of dark many flavor QCD and show that the model undergoes ultra-supercooling due to the produced strong first-order thermal phase transition along the (approximately realized) flat direction based on the Gildener-Weinberg mechanism. Passing through evaluation of the bubble nucleation/percolation, we address the reheating and relaxation processes, which are generically non-thermal and nonadiabatic. Parametrizing the reheating epoch in terms of the e-folding number, we propose proper formulae for the redshift effects on the GW frequencies and signal spectra. It then turns out that the ultra-supercooling predicted from the Higgs-portal scalegenesis generically yields none of GW signals with the frequencies as low as nano Hz, unless the released latent heat is transported into another sector other than reheating the universe. Instead, models of this class prefer to give the higher frequency signals and still keeps the future prospected detection sensitivity, like at LISA, BBO, and DECIGO, etc. We also find that with large flavors in the dark sector, the GW signals are made further smaller and the peak frequencies higher. Characteristic phenomenological consequences related to the multiple chiral scalars include the prediction of dark pions with the mass much less than TeV scale, which is also briefly addressed.