Frequency Trajectories Research Articles

Prospective associations between stressors and alcohol use from early adolescence to young adulthood in Mexican-origin youth in the United States.

Stressors experienced across multiple domains (e.g., family and peers) may contribute to alcohol use trajectories; however, little is known about the longitudinal links between stressors and alcohol use among Latinx youth. Guided by prior work on stressors and alcohol use, the present study used longitudinal data to examine whether Mexican-origin adolescents' (N = 674; 50% female; 28% Mexico born; 72% U.S. born) experiences of family and peer stressors across early to middle adolescence (Mage = 10.86, SDage = 0.51) predicted trajectories of alcohol use frequency and binge drinking from middle adolescence to young adulthood (Mage = 23.17, SDage = 0.59). Using two strategies for modeling stressors, we report results that showed more support for stressors across early adolescence as predictors of alcohol use trajectories when stressors were modeled as growth trajectories versus modeled as distal and proximal stressors. Findings underscore the need to consider strategies to attenuate the longitudinal links between stressors and alcohol use among Mexican-origin youth. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Analysis of Fractional Resonant Controllers for Voltage-Controlled Applications

This paper investigates the application of fractional proportional–resonant controllers within the voltage control loop of grid-forming inverters. The use of such controllers introduces an additional degree of freedom, enabling greater flexibility in manipulating frequency trajectories. This flexibility can be harnessed to improve tracking error and enhance disturbance rejection, particularly in applications requiring precise voltage regulation. The paper conducts a conceptual stability analysis of ideal fractional proportional–resonant controllers using the Nyquist criterion. A tuning procedure based on robustness criteria for the proposed controller is also addressed. This tuning strategy is used to compare different controllers under the same conditions. In addition, a sensitivity analysis is provided, comparing the performance of fractional proportional–resonant controllers with traditional proportional–resonant controllers equipped with harmonic compensation. The controller’s formulation and performance are validated through simulations and tested with a 20 kVA inverter under high non-linear loads. Compared to classical control approaches, the fractional tuning parameter enhances tracking performance, reduces phase delay, and improves disturbance rejection. These improvements are achieved with a controller designed to minimise computational demands in terms of memory usage and execution time.

Binge drinking trajectories across adolescence and early adulthood: Associations with genetic influences for dual-systems impulsive personality traits, alcohol consumption, and alcohol use disorder.

Binge drinking is a relatively common pattern of alcohol use among youth with normative frequency trajectories peaking in emerging and early adulthood. Frequent binge drinking is a critical risk factor for not only the development of alcohol use disorders (AUDs) but also increased odds of alcohol-related injury and death, and thus constitutes a significant public health concern. Changes in binge drinking across development are strongly associated with changes in impulsive personality traits (IPTs) which have been hypothesized as intermediate phenotypes associated with genetic risk for heavy alcohol use and AUD. The current study sought to examine the extent to which longitudinal changes in binge drinking and intoxication frequency across adolescence and early adulthood are associated with genetic influences underlying dual-systems IPTs (i.e., top-down [lack of self-control] and bottom-up [sensation seeking and urgency] constructs) alongside genetic risk for alcohol consumption and AUD. Associations were tested using conditional latent growth curve polygenic score (PGS) models in three independent longitudinal samples ( N =10,554). Results suggested consistent significant and independent associations across all samples between sensation seeking PGSs and model intercepts (i.e., higher frequency of binge drinking at first measurement occasion) and alcohol consumption PGSs and model slopes (i.e., steeper increases toward peak binge drinking frequency). Urgency PGSs were not significantly associated with changes in binge drinking or intoxication frequency. Collectively, these findings highlight the role of unique but correlated IPT and alcohol-specific genetic factors in the emergence and escalation of binge drinking during adolescence and early adulthood.

Effect of shaft deflection on the dynamic behaviors of the ceramic bearing rotor system within wide temperature ranges

The shaft deflection has great impact on the dynamic characteristics of the ceramic bearing rotor system, which cannot be ignored within wide temperature ranges. This paper establishes a model considering the shaft deflection, and the impact of shaft deflection appears along with changes in bearing attitude. The interaction between the various components of the bearing is recalculated, and the dynamic behavior of the full ceramic bearing rotor system in wide temperature ranges is obtained based on the thermal deformation difference. The trends of dynamic behaviors with rotor mass, rotation speed and shaft length-diameter ratio are investigated, and comparisons between simulation and experimental results are conducted for verification. It is found that the shaft deflection makes impact by bringing in extra centrifugal force, and the effects are reflected in frequency components and trajectories. The simulation results with shaft deflection fit well with the experimental results, which proves that the dynamic model considering the shaft deflection better illustrates the dynamic behavior of the ceramic outer ring when the contact situations change with working temperature. The research carries out deep insight into the running of ceramic bearing rotor system, and provides guidance for the improvement of dynamic behaviors in wide temperature ranges.

Investigation on dynamic rubbing characteristics of a bladed rotor system with multi-mode rubbing fault

Dynamic behavior analysis of blades under high cycle fatigue with rubbing fault is vital for estimating the reliability of rotating machinery. This paper focuses on a novel rubbing model and the flexible dynamic characteristics of a shaft-blade-disk system (SBDS) model with considerations of various forms of rub-impacts. Firstly, the deformation relations and the motion equations of the SBDS model in steady state are derived using Lagrange's principle. Secondly, a new quasi-static blade rubbing model involving shaft deformation and blade coupling effect is established, and the influence of rub-impact correlation parameters on rubbing force is further explored. Then, both the effect of shaft-disk coupling on the blades at various states and the dynamic characteristics of numerous rubbing modes are also investigated. The results revealed that the asymmetric form of rub-impact will induce more complex vibrational behavior (such as frequency components and trajectory differences), while the symmetrical rubbing modes can keep the system relatively stable.

Model-Free Fast Frequency Support of Wind Farms for Tracking Optimal Frequency Trajectory

Model-Free Fast Frequency Support of Wind Farms for Tracking Optimal Frequency Trajectory

Deep estimation of the intensity and timing of natural selection from ancient genomes.

Leveraging past allele frequencies has proven to be key for identifying the impact of natural selection across time. However, this approach suffers from imprecise estimations of the intensity (s) and timing (T) of selection, particularly when ancient samples are scarce in specific epochs. Here, we aimed to bypass the computation of allele frequencies across arbitrarily defined past epochs and refine the estimations of selection parameters by implementing convolutional neural networks (CNNs) algorithms that directly use ancient genotypes sampled across time. Using computer simulations, we first show that genotype-based CNNs consistently outperform an approximate Bayesian computation (ABC) approach based on past allele frequency trajectories, regardless of the selection model assumed and the number of available ancient genotypes. When applying this method to empirical data from modern and ancient Europeans, we replicated the reported increased number of selection events in post-Neolithic Europe, independently of the continental subregion studied. Furthermore, we substantially refined the ABC-based estimations of s and T for a set of positively and negatively selected variants, including iconic cases of positive selection and experimentally validated disease-risk variants. Our CNN predictions support a history of recent positive and negative selection targeting variants associated with host defence against pathogens, aligning with previous work that highlights the significant impact of infectious diseases, such as tuberculosis, in Europe. These findings collectively demonstrate that detecting the footprints of natural selection on ancient genomes is crucial for unravelling the history of severe human diseases.

Attenuation of non-stationary random noise in ground penetrating radar data based on time-varying filtering

The attenuation of random noise is significant in the enhancement of the signal-to-noise ratio in ground penetrating radar (GPR) data. However, the non-stationary nature of random noise poses significant challenges to most conventional denoising methods. We propose a time-varying bandpass filter (TVBF) based on the local maxima multiple synchrosqueezing transform (LMMSST), referred to as TVBFLMMSST, for the attenuation of non-stationary random noise in GPR data. The LMMSST method is introduced to achieve highly compressed time–frequency representation (TFR). By integrating an instantaneous frequency trajectory detection algorithm, we design the TVBF to successfully separate useful signals from random noise. The efficacy of the TVBFLMMSST is validated through synthetic and field experiments, demonstrating its capability to effectively isolate components of useful signals and noise signals. Compared to classical denoising methods, TVBFLMMSST exhibits robust performance in attenuating random noise while preserving the amplitude of useful signals.

Population genetic admixture and evolutionary history in the Shandong Peninsula inferred from integrative modern and ancient genomic resources

BackgroundAncient northern East Asians (ANEA) from the Yellow River region, who pioneered millet cultivation, play a crucial role in understanding the origins of ethnolinguistically diverse populations in modern China and the entire landscape of deep genetic structure and variation discovery in modern East Asians. However, the direct links between ANEA and geographically proximate modern populations, as well as the biological adaptive processes involved, remain poorly understood.ResultsHere, we generated genome-wide SNP data for 264 individuals from geographically different Han populations in Shandong. An integrated genomic resource encompassing both modern and ancient East Asians was compiled to examine fine-scale population admixture scenarios and adaptive traits. The reconstruction of demographic history and hierarchical clustering patterns revealed that individuals from the Shandong Peninsula share a close genetic affinity with ANEA, indicating long-term genetic continuity and mobility in the lower Yellow River basin since the early Neolithic period. Biological adaptive signatures, including those related to immune and metabolic pathways, were identified through analyses of haplotype homozygosity and allele frequency spectra. These signatures are linked to complex traits such as height and body mass index, which may be associated with adaptations to cold environments, dietary practices, and pathogen exposure. Additionally, allele frequency trajectories over time and a haplotype network of two highly differentiated genes, ABCC11 and SLC10A1, were delineated. These genes, which are associated with axillary odor and bilirubin metabolism, respectively, illustrate how local adaptations can influence the diversification of traits in East Asians.ConclusionsOur findings provide a comprehensive genomic dataset that elucidates the fine-scale genetic history and evolutionary trajectory of natural selection signals and disease susceptibility in Han Chinese populations. This study serves as a paradigm for integrating spatiotemporally diverse ancient genomes in the era of population genomic medicine.

Allele frequency dynamics under sex-biased demography and sex-specific inheritance in a pedigreed jay population.

Sex-biased demography, including sex-biased survival or migration, can alter allele frequency changes across the genome. In particular, we can expect different patterns of genetic variation on autosomes and sex chromosomes due to sex-specific differences in life histories, as well as differences in effective population size, transmission modes, and the strength and mode of selection. Here, we demonstrate the role that sex differences in life history played in shaping short-term evolutionary dynamics across the genome. We used a 25-year pedigree and genomic dataset from a long-studied population of Florida Scrub-Jays (Aphelocoma coerulescens) to directly characterize the relative roles of sex-biased demography and inheritance in shaping genome-wide allele frequency trajectories. We used gene dropping simulations to estimate individual genetic contributions to future generations and to model drift and immigration on the known pedigree. We quantified differential expected genetic contributions of males and females over time, showing the impact of sex-biased dispersal in a monogamous system. Due to female-biased dispersal, more autosomal variation is introduced by female immigrants. However, due to male-biased transmission, more Z variation is introduced by male immigrants. Finally, we partitioned the proportion of variance in allele frequency change through time due to male and female contributions. Overall, most allele frequency change is due to variance in survival and births. Males and females make similar contributions to autosomal allele frequency change, but males make higher contributions to allele frequency change on the Z chromosome. Our work shows the importance of understanding sex-specific demographic processes in characterizing genome-wide allele frequency change in wild populations.

Modeling and vibration analysis of bolted composite conical-conical shells with flanges

This paper proposes a general semi-analytical modeling method of bolted composite conical-conical shells with flanges based on the first-order shear deformation theory. The coupling connections between conical shells and flanges are achieved by using the penalty function method. A bolted joint model that considers the non-uniform distribution of interface pressure is proposed, which can efficiently simulate any number of bolt connections between flanges and boundaries and between flanges and flanges. The correctness and effectiveness of the proposed modeling method are confirmed through step-by-step comparisons with finite element analysis and experimental testing. Then, the influence of various parameters on the frequency trajectories and modal shapes of bolted composite conical-conical shells with flanges is analyzed, with a particular focus on frequency veering and modal coupling vibration behaviors in these structures. The analyses indicate that the proposed modeling method can flexibly adjust structural geometric parameters, material parameters, bolt parameters, fiber layer parameters, and so on, which can efficiently guide the dynamic design of bolted composite conical shell structures with flanges.

Critically-ill COVID-19 susceptibility gene CCR3 shows natural selection in sub-Saharan Africans

The prevalence of COVID-19 critical illness varies across ethnicities, with recent studies suggesting that genetic factors may contribute to this variation. The aim of this study was to investigate natural selection signals of genes associated with critically-ill COVID-19 in sub-Saharan Africans. Severe COVID-19 SNPs were obtained from the HGI website. Selection signals were assessed in 661 sub-Sahara Africans from 1000 Genomes Project using integrated haplotype score (iHS), cross-population extended haplotype homozygosity (XP-EHH), and fixation index (Fst). Allele frequency trajectory analysis of ancient DNA samples were used to validate the existing of selection in sub-Sahara Africans. We also used Mendelian randomization to decipher the correlation between natural selection and critically-ill COVID-19. We identified that CCR3 exhibited significant natural selection signals in sub-Sahara Africans. Within the CCR3 gene, rs17217831-A showed both high iHS (Standardized iHS = 2) and high XP-EHH (Standardized XP-EHH = 2.5) in sub-Sahara Africans. Allele frequency trajectory of CCR3 rs17217831-A revealed natural selection occurring in the recent 1,500 years. Natural selection resulted in increased CCR3 expression in sub-Sahara Africans. Mendelian Randomization provided evidence that increased blood CCR3 expression and eosinophil counts lowered the risk of critically ill COVID-19. Our findings suggest that sub-Saharan Africans are resistant to critically ill COVID-19 due to natural selection and identify CCR3 as a potential novel therapeutic target.

SingDistVis: interactive Overview+Detail visualization for F0 trajectories of numerous singers singing the same song

AbstractThis paper describes SingDistVis, an information visualization technique for fundamental frequency (F0) trajectories of large-scale singing data where numerous singers sing the same song. SingDistVis allows to explore F0 trajectories interactively by combining two views: OverallView and DetailedView. OverallView visualizes a distribution of the F0 trajectories of the song in a time-frequency heatmap. When a user specifies an interesting part, DetailedView zooms in on the specified part and visualizes singing assessment (rating) results. Here, it displays high-rated singings in red and low-rated singings in blue. When the user clicks on a particular singing, the audio source is played and its F0 trajectory through the song is displayed in OverallView. We selected heatmap-based visualization for OverallView to provide an overview of a large-scale F0 dataset, and polyline-based visualization for DetailedView to provide a more precise representation of a small number of particular F0 trajectories. This paper introduces a subjective experiment using 1,000 singing voices to determine suitable visualization parameters. Then, this paper presents user evaluations where we asked participants to compare visualization results of four types of Overview+Detail designs and concluded that the presented design archived better evaluations than other designs in all the seven questions. Finally, this paper describes a user experiment in which eight participants compare SingDistVis with a baseline implementation in exploring interested singing voices and concludes that the proposed SingDistVis archived better evaluations in nine of the questions.

Optimizing quantum gates towards the scale of logical qubits

A foundational assumption of quantum error correction theory is that quantum gates can be scaled to large processors without exceeding the error-threshold for fault tolerance. Two major challenges that could become fundamental roadblocks are manufacturing high-performance quantum hardware and engineering a control system that can reach its performance limits. The control challenge of scaling quantum gates from small to large processors without degrading performance often maps to non-convex, high-constraint, and time-dynamic control optimization over an exponentially expanding configuration space. Here we report on a control optimization strategy that can scalably overcome the complexity of such problems. We demonstrate it by choreographing the frequency trajectories of 68 frequency-tunable superconducting qubits to execute single- and two-qubit gates while mitigating computational errors. When combined with a comprehensive model of physical errors across our processor, the strategy suppresses physical error rates by ~3.7× compared with the case of no optimization. Furthermore, it is projected to achieve a similar performance advantage on a distance-23 surface code logical qubit with 1057 physical qubits. Our control optimization strategy solves a generic scaling challenge in a way that can be adapted to a variety of quantum operations, algorithms, and computing architectures.

The pseudoentropy of allele frequency trajectories, the persistence of variation, and the effective population size

To concisely describe how genetic variation, at individual loci or across whole genomes, changes over time, and to follow transitory allelic changes, we introduce a quantity related to entropy, that we term pseudoentropy. This quantity emerges in a diffusion analysis of the mean time a mutation segregates in a population. For a neutral locus with an arbitrary number of alleles, the mean time of segregation is generally proportional to the pseudoentropy of initial allele frequencies. After the initial time point, pseudoentropy generally decreases, but other behaviours are possible, depending on the genetic diversity and selective forces present.For a biallelic locus, pseudoentropy and entropy coincide, but they are distinct quantities with more than two alleles. Thus for populations with multiple biallelic loci, the language of entropy suffices. Then entropy, combined across loci, serves as a concise description of genetic variation. We used individual based simulations to explore how this entropy behaves under different evolutionary scenarios. In agreement with predictions, the entropy associated with unlinked neutral loci decreases over time. However, deviations from free recombination and neutrality have clear and informative effects on the entropy’s behaviour over time.Analysis of publicly available data of a natural D. melanogaster population, that had been sampled over seven years, using a sliding-window approach, yielded considerable variation in entropy trajectories of different genomic regions. These mostly follow a pattern that suggests a substantial effective population size and a limited effect of positive selection on genome-wide diversity over short time scales.

Minimum Energy Demands of Energy Storages for Fast Frequency Response: Formulation, Solution, and Implementation

Energy storage (ES) is a kind of promising but costly fast-frequency-response (FFR) resource in low-inertia power systems. This paper addresses the minimum demand of a power system for energy capacities of ES to providing sufficient frequency support, including the formulation, the optimal solution, and its practical implementation. First, the minimum energy control problem of ESs for providing FFR with transient frequency constraints is formulated and then converted into an optimal frequency trajectory planning counterpart. This transformation makes the transient frequency constraint tractable and allows to identify the optimal frequency trajectory that minimizes energy demand for ESs, which leads to an analytical expression of the system minimum demand for ESs energy capacities to ensure frequency security. Moreover, by unfolding the optimal frequency trajectory, the optimal control strategy of ESs is derived explicitly for the first time. The theoretical result directs to a practical controller implementation, which is referred to as the decentralized optimal energy frequency control (DOEFC). Finally, simulations on the modified IEEE 39- bus system verify our results and demonstrate the superiority of the DOEFC in reducing the energy capacity demand of ESs, defending successive disturbances, and hedging against parameter errors.

Enhanced adaptive linear chirplet transform for crossing frequency trajectories

Time-frequency representation (TFR) is of great significance for capturing and analyzing nonlinear or nonstationary multi-component signals. The adaptive linear chirplet transform (ALCT) has proven effective for dealing with signals with frequencies that intersect, providing accurate estimations of instantaneous frequencies (IFs) for both intersecting and non-intersecting components. However, challenges still exist when handling signal components that have overlapping amplitudes, and when performing reconstruction, the components are mixed around the time of the intersection. To overcome these issues, we present the enhanced ALCT (EALCT) approach that synergistically combines several methods to overcome the previously described issues with the ALCT algorithm. The EALCT identifies the IFs by a modified ACLT as a set and separates them using the ridge path regrouping (RPRG) algorithm. Subsequently, the IFs and the corresponding chirp-rates of each component are employed to reconstruct the signal using an algorithm based on the chirplet transform. By implementing this approach, the EALCT accurately reconstructs the components with intersecting amplitudes at the time of intersection as well as the original signal. The EALCT also maintains the performance of the ALCT when applied to signals that do not have components with intersecting amplitudes, such that the proposed approach does not detract from existing success. The power of the EALCT is demonstrated using analytical and experimentally measured nonlinear and nonstationary signals and the results are compared with those produced by the ALCT.

Application of second order multi-synchrosqueezing transform for seismic data analysis

Time-frequency methods, which allow us to observe the seismic signal in time and frequency domains simultaneously, can efficiently discern the seismic response features of geological bodies in different frequency bands. In this paper, a newly developed time-frequency method named second order multi-synchrosqueezing transform (SMSST) is introduced to analyze seismic data. SMSST combines the second-order instantaneous frequency estimation and the iterative idea of multi-synchrosqueezing transform to approximate the true instantaneous frequency trajectory. It solves the problems of non-reassignment points and the sensitivity to noise at the same time. Synthetic example is employed to validate the superiority of SMSST in time-frequency analysis. Results obtained from four different methods show that SMSST offer the best performance in terms of energy-concentration and noise robustness. Applications on field data further demonstrate that SMSST-based method is suitable and effective to highlight geological information and stratigraphic features compared to the previous techniques. The capabilities of SMSST for accurate reservoir localization with high resolution, making it a promising and potential tool in seismic interpretation.

Disturbance Frequency Trajectory Prediction in Power Systems Based on LightGBM Spearman

Addressing the issue of reduced system inertia and significantly increased risk of system frequency deviation due to high penetration of renewable energy sources, this paper proposes a power system disturbance frequency trajectory prediction method based on light gradient boosting machine (LightGBM) Spearman to provide data support for advanced system stability judgment and the initiation of stability control measures. Firstly, the optimal cluster is determined by combining the K-means clustering algorithm with the elbow method to eliminate redundant electrical quantities. Subsequently, the Spearman coefficient is used to analyze feature correlation and filter out electrical quantities that are strongly correlated with frequency stability. Finally, a frequency trajectory prediction model is built based on LightGBM to achieve accurate prediction of disturbed frequency trajectories. The method is validated using a case study on the New England 10-machine 39-bus system constructed on the CloudPSS 4.0 full electromagnetic cloud simulation platform, and the results show that the proposed method has high accuracy in frequency trajectory prediction.

Nowcasting the Vehicular Control Delay From Low-Ping Frequency Trajectories via Incremental Hypergraph Learning

Nowcasting the Vehicular Control Delay From Low-Ping Frequency Trajectories via Incremental Hypergraph Learning