High Time Variability Research Articles

Linking the neural signature of response time variability to Alzheimer's disease pathology and cognitive functioning.

Promising evidence has suggested potential links between mind-wandering and Alzheimer's disease (AD). Yet, older adults with diagnosable neurocognitive disorders show reduced meta-awareness, thus questioning the validity of probe-assessed mind-wandering in older adults. In prior work, we employed response time variability as an objective, albeit indirect, marker of mind-wandering to identify patterns of functional connectivity that predicted mind-wandering. In the current study, we evaluated the association of this connectome-based, mind-wandering model with cerebral spinal fluid (CSF) p-tau/Aβ 42 ratio in 289 older adults from the Alzheimer's Disease NeuroImaging Initiative (ADNI). Moreover, we examined if this model was similarly associated with individual differences in composite measures of global cognition, episodic memory, and executive functioning. Edges from the high response time variability model were significantly associated with CSF p-tau/Aβ ratio. Furthermore, connectivity strength within edges associated with high response time variability was negatively associated with global cognition and episodic memory functioning. This study provides the first empirical support for a link between an objective neuromarker of mind-wandering and AD pathophysiology. Given the observed association between mind-wandering and cognitive functioning in older adults, interventions targeted at reducing mind-wandering, particularly before the onset of AD pathogenesis, may make a significant contribution to the prevention of AD-related cognitive decline.

A resource prediction method for air traffic cyber-physical-social system

Air traffic is exhibiting the characteristics of large flow, strong coupling, and high time variation. Therefore, the complex network of air traffic is more vulnerable to disturbances. When it is disturbed, the failure of some nodes spreads through dependency relationships in the network, resulting in cascade failure. In the event of a cascade failure, the network may quickly collapse until it is paralyzed, with widespread delays and flight cancellations. The current flow management and deployment methods still remain in the control-oriented stage, which is mainly completed by air traffic controls (ATCs), and lack of accurate flow adjustment and effective utilization of capacity. The whole air traffic system and its peripheral factors are intricate, so human and social factors must be integrated into the control and decision-making of the system. Considering engineering and social factors such as operation environment, social environment, personnel, rules, equipment, and information processing, we analyse the air traffic in a cyber-physical-social system (CPSS). To reflect the actual system behaviour rules, dynamic response, limit state, and so on, the corresponding computational experiment and comprehensive evaluation system are established. Based on neural networks and other technologies, a resource prediction scheme based on task demand is proposed for multi-dimensional resources such as airports, air routes, and ATC, to reduce the cost of system resource scheduling and improve resource utilization through resource prediction and adjustment. Finally, the accuracy of the proposed resource prediction algorithm is verified by theoretical analysis and simulation.

A Vehicle Velocity Prediction Method with Kinematic Segment Recognition

Accurate vehicle velocity prediction is of great significance in vehicle energy distribution and road traffic management. In light of the high time variability of vehicle velocity itself and the limitation of single model prediction, a velocity prediction method based on K-means-QPSO-LSTM with kinematic segment recognition is proposed in this paper. Firstly, the K-means algorithm was used to cluster samples with similar characteristics together, extract kinematic fragment samples in typical driving conditions, calculate their feature parameters, and carry out principal component analysis on the feature parameters to achieve dimensionality reduction transformation of information. Then, the vehicle velocity prediction sub-neural network models based on long short-term memory (LSTM) with the QPSO algorithm optimized were trained under different driving condition datasets. Furthermore, the kinematic segment recognition and traditional vehicle velocity prediction were integrated to form an adaptive vehicle velocity prediction method based on driving condition identification. Finally, the current driving condition type was identified and updated in real-time during vehicle velocity prediction, and then the corresponding sub-LSTM model was used for vehicle velocity prediction. The simulation experiment demonstrated a significant enhancement in both the velocity and accuracy of prediction through the proposed method. The proposed hybrid method has the potential to improve the accuracy and reliability of vehicle velocity prediction, making it applicable in various fields such as autonomous driving, traffic management, and energy management strategies for hybrid electric vehicles.

INTRAINDIVIDUAL VARIABILITY IN SLEEP DURATION IS ASSOCIATED WITH HIPPOCAMPAL VOLUME IN MID LIFE

Abstract Sleep is associated with global cognition, consolidation of memories, and structural & functional integrity of areas of the brain that support healthy cognition. While there has been research on sleep in older adults that indicates associations between poor sleep, cognitive decline, memory performance, and changes in the brain, there has been comparatively little work assessing the impact of sleep on the structure of the brain during middle adulthood. Changes in the brain begin much earlier than older adulthood, with midlife seeing cortical thinning and a decrease in brain volume in areas that support healthy cognitive functioning and memory, such as the hippocampus. Understanding how sleep may contribute to hippocampal volume in midlife may help inform how changing poor sleep is important for supporting successful aging. The current study examines the associations between 7 days of actigraphy measured sleep and hippocampal volume (MRI) in 82 midlife adults (Mage = 49.13 years, SD = 11.74; 28-76 years old) who completed the Biomarker and Neuroscience projects in the Midlife in the United States Refresher study. Results indicated that bi-lateral hippocampal volume was negatively associated with high (vs low) intraindividual total sleep time variability (>= 60 min; mean average deviation), with a 280 and 252 mm3 (p’s = .006 & .035) volume reduction for left and right hemispheres respectively. This relationship was not seen for other measures of sleep quality and the analysis additionally controlled for age, sex, BMI, race, depression status, education, and subjective physical & mental/emotional health.

Measuring the Relevance of Factors on Cross-Sectional Returns with Decision Trees

This study is concerned with new ways to identify and analyse the factors on cross-sectional returns in financial markets with respect to their time-variability. Therefore, classification and regression trees and conventional regression models are applied. This study uses data on the S&P 500 from 1999 to 2019. Empirical findings show high time variability of factors on cross-sectional returns. The high level of time-variability is not dependent on the applied model. It is also shown that CARTs and conventional regression models have low power when it comes to identifying the factors on cross-sectional returns or predicting the returns themself.

Adaptive Beamforming Approaches to Improve Passive Radar Performance in Sea and Wind Farms' Clutter.

This article presents the problem of passive radar vessel detection in a real coastal scenario in the presence of sea and wind farms’ clutter, which are characterised by high spatial and time variability due to the influence of weather conditions. Deterministic and adaptive beamforming techniques are proposed and evaluated using real data. Key points such as interference localisation and characterisation are tackled in the passive bistatic scenario with omnidirectional illuminators that critically increase the area of potential clutter sources to areas far from the surveillance area. Adaptive beamforming approaches provide significant Signal-to-Interference improvements and important radar coverage improvements. In the presented case study, an aerial target is detected 28 km far from the passive radar receiver, fulfilling highly demanding performance requirements.

Method for Solving the Microwave Heating Temperature Distribution of the TE10 Mode

Microwave heating is a process in which the electric, magnetic, and temperature fields are coupled with each other and are characterised by strong non-linearity, high time variability, and infinite dimensionality. This paper proposes a method for predicting the microwave heating temperature distribution of the TE10 mode, because the traditional numerical calculation method is not conducive to designing microwave controllers. First, the spatial distribution of the main electromagnetic mode TE10 waves in a rectangular waveguide was analysed using the principal mode analysis method. An expression for the transient dissipated power and a heat balance equation with infinite-dimensional characteristics were constructed. Then, the microwave heating model was decomposed into electromagnetic and temperature field submodels. A time discretization approach was used to approximate the transient constant dielectric constant. The heating medium was meshed to solve the electric field strength and transient dissipated power in discrete domains, and the temperature distribution was obtained by substituting this value into the finite-dimensional temperature field submodel. Finally, the validity of the proposed numerical model was verified by comparing the results with the numerical results obtained with the conventional finite element method. The methodology presented in this paper provides a solid basis for designing microwave heating controllers.

Kinematic characteristics during gait in frail older women identified by principal component analysis

Frailty is associated with gait variability in several quantitative parameters, including high stride time variability. However, the associations between joint kinematics during walking and increased gait variability with frailty remain unclear. In the current study, principal component analysis was used to identify the key joint kinematics characteristics of gait related to frailty. We analyzed whole kinematic waveforms during the entire gait cycle obtained from the pelvis and lower limb joint angle in 30 older women (frail/prefrail: 15 participants; non-frail: 15 participants). Principal component analysis was conducted using a 60 × 1224 input matrix constructed from participants’ time-normalized pelvic and lower-limb-joint angles along three axes (each leg of 30 participants, 51 time points, four angles, three axes, and two variables). Statistical analyses revealed that only principal component vectors 6 and 9 were related to frailty. Recombining the joint kinematics corresponding to these principal component vectors revealed that frail older women tended to exhibit greater variability of knee- and ankle-joint angles in the sagittal plane while walking compared with non-frail older women. We concluded that greater variability of knee- and ankle-joint angles in the sagittal plane are joint kinematic characteristics of gait related to frailty.

Fair and profitable: How pricing and lead-time quotation policies can help

In this paper, we propose four policies to serve price and lead-time sensitive customers with a single type of product produced in an M/GI/1 type make-to-stock queueing system. The policies are developed to observe certain principles of fairness: if a customer is quoted a longer lead-time, she/he must be charged a lower price and a significant proportion of the deliveries has to be made during the quoted lead times. Although handling non-exponential service times in this setting presents difficulties, our analysis of the proposed policies is exact. Two of the policies operate with static prices, while one of the two dynamic pricing policies also quotes dynamic lead times. By construction of the policies, we show that the dynamic pricing policies are more profitable. Numerical examples bring additional light by showing that in small markets with oversensitive customers, dynamic policies can be profitable while static pricing policies can fail. In a larger market, a simple dynamic policy charging two prices depending on the stock availability can be a reasonable compromise. Dynamic policies tend to suffer less against high production time variability as well.

Processing of Rainfall Time Series Data in the State of Rio de Janeiro

The goal was to perform the filling, consistency and processing of the rainfall time series data from 1943 to 2013 in five regions of the state. Data were obtained from several sources (ANA, CPRM, INMET, SERLA and LIGHT), totaling 23 stations. The time series (raw data) showed failures that were filled with data from TRMM satellite via 3B43 product, and with the climatological normal from INMET. The 3B43 product was used from 1998 to 2013 and the climatological normal over the 1947- 1997 period. Data were submitted to descriptive and exploratory analysis, parametric tests (Shapiro-Wilks and Bartlett), cluster analysis (CA), and data processing (Box Cox) in the 23 stations. Descriptive analysis of the raw data consistency showed a probability of occurrence above 75% (high time variability). Through the CA, two homogeneous rainfall groups (G1 and G2) were defined. The group G1 and G2 represent 77.01% and 22.99% of the rainfall occurring in SRJ, respectively. Box Cox Processing was effective in stabilizing the normality of the residuals and homogeneity of variance of the monthly rainfall time series of the five regions of the state. Data from 3B43 product and the climatological normal can be used as an alternative source of quality data for gap filling.

Large broad-leaved canopy of banana (Musa nana Lour.) induces dramatically high spatial–temporal variability of throughfall

Abstract Throughfall (TF) is an important water input of rainfall redistribution into floor, and its spatial–temporal variability under some species' canopies has been documented to evaluate effect on splash erosion. However, the understanding of TF variability under large broad-leaved canopy remains insufficient. In this study, the spatial heterogeneity, temporal stability and drop size of TF were quantified using variogram fitting, normalised ranking and filter paper staining, respectively, under banana (Musa nana Lour.) canopy comprising long and wide leaves. Results indicated TF pattern showed strong spatial correlation at a range of 3–5 m. High spatial variability of TF was found, which was affected by rainfall event size and was accompanied by great canopy disturbance. TF plots revealed high time variability, which was mainly controlled by unstable banana canopy structure. TF drop size from leaf dripping points varied in 3–10 mm and showed significant differences (p < 0.05) among five kinds of leaf shapes, implying that concave and broken banana leaves were involved in the variability of TF drop size. Overall, results demonstrate the spatial–temporal variability of TF is dramatically induced by banana canopy with broad leaves, which may result in non-uniform soil water content and splash erosion under the canopy.

In-Orbit Measurements and Analysis of Radio Interference in the UHF Amateur Radio Band from the LUME-1 Satellite

Radio interference in the uplink makes communication to satellites in the UHF amateur radio band (430–440 MHz) challenging for any satellite application. Interference measurements and characterisation can improve the robustness and reliability of the communication system design. Most published results focus on average power spectrum measurements and heatmaps. We apply a low complexity estimator on an SDR (Software-Defined Radio) to study the interference’s dispersion and temporal variation on-board a small satellite as an alternative. Measuring the Local Mean Envelope (LME) variability with different averaging window lengths enables the estimation of time variability of the interference. The coefficient of variation for the LME indicates how much the signals vary in time and the spread in magnitudes. In this article, theoretical analysis, simulations, and laboratory results were used to validate this measurement method. In-orbit measurements were performed on-board the LUME-1 satellite. Band-limited interference with pulsed temporal behaviour and a high coefficient of variation was detected over North America, Europe, and the Arctic, where space-tracking radars are located. Wide-band pulsed interference with high time variability was also detected over Europe. These measurements show why operators that use a communication system designed for Additive White Gaussian Noise (AWGN) at power levels obtained from heatmaps struggle to command their satellites.

Cloud activation properties of aerosol particles in a continental Central European urban environment

Abstract. Collocated measurements using a condensation particle counter, differential mobility particle sizer and cloud condensation nuclei counter were realised in parallel in central Budapest from 15 April 2019 to 14 April 2020 to gain insight into the cloud activation properties of urban aerosol particles. The median total particle number concentration was 10.1 × 103 cm−3. The median concentrations of cloud condensation nuclei (CCN) at water vapour supersaturation (S) values of 0.1 %, 0.2 %, 0.3 %, 0.5 % and 1.0 % were 0.59, 1.09, 1.39, 1.80 and 2.5 × 103 cm−3, respectively. The CCN concentrations represented 7–27 % of all particles. The CCN concentrations were considerably larger but the activation fractions were systematically substantially smaller than observed in regional or remote locations. The effective critical dry particle diameters (dc,eff) were derived utilising the CCN concentrations and particle number size distributions. Their median values at the five supersaturation values considered were 207, 149, 126, 105 and 80 nm, respectively; all of these diameters were positioned within the accumulation mode of the typical particle number size distribution. Their frequency distributions revealed a single peak for which the geometric standard deviation increased monotonically with S. This broadening indicated high time variability in the activating properties of smaller particles. The frequency distributions also showed fine structure, with several compositional elements that seemed to reveal a consistent or monotonical tendency with S. The relationships between the critical S and dc,eff suggest that urban aerosol particles in Budapest with diameters larger than approximately 130 nm showed similar hydroscopicity to corresponding continental aerosol particles, whereas smaller particles in Budapest were less hygroscopic than corresponding continental aerosol particles. Only modest seasonal cycling in CCN concentrations and activation fractions was seen, and only for large S values. This cycling likely reflects changes in the number concentration, chemical composition and mixing state of the particles. The seasonal dependencies of dc,eff were featureless, indicating that the droplet activation properties of the urban particles remained more or less the same throughout the year. This is again different from what is seen in non-urban locations. Hygroscopicity parameters (κ values) were computed without determining the time-dependent chemical composition of the particles. The median values for κ were 0.15, 0.10, 0.07, 0.04 and 0.02, respectively, at the five supersaturation values considered. The averages suggested that the larger particles were considerably more hygroscopic than the smaller particles. We found that the κ values for the urban aerosol were substantially smaller than those previously reported for aerosols in regional or remote locations. All of these characteristics can be linked to the specific source composition of particles in cities. The relatively large variability in the hygroscopicity parameters for a given S emphasises that the individual values represent the CCN population in ambient air while the average hygroscopicity parameter mainly corresponds to particles with sizes close to the effective critical dry particle diameter.

Crash modification functions for pavement surface condition and geometric design indicators

The combined contribution to highway safety of pavement surface and geometric design indicators is not well investigated due to the complexity of data collection and high time variability of pavement surface conditions. Introduction of high efficiency equipment for comprehensive road surveys is mitigating this issue, expanding possibilities of data integration. In this framework, the present study developed crash modification functions (CMFs) of pavement surface and geometric design indicators for different crash types (total, run-off-the-road, and others), pavement conditions (dry and wet), and lighting conditions (daytime and nighttime) based on data from two-lane rural highways in Italy. Geometric and pavement data were surveyed with the Automatic Road Analyzer and the Grip Tester. Pavement surface condition data were corrected to the crash time by pavement performance deterioration models based on traffic load to account for the variation in pavement conditions over time. Crash, traffic and weather data were retrieved from national and local databases. This study used safety performance functions (SPFs), fitted with generalized linear modelling techniques and a negative binomial distribution error structure, for developing CMFs. The SPFs were used to quantify the effect of a specific variable on crash occurrence and CMFs were then derived from the model coefficients. CMFs were developed for the following parameters: Grip Number, International Roughness Index, curvature change ratio, coefficient of variation of the curvature, maximum superelevation deficiency, and minimum lane width.According to the study results, an increase in friction, as measured by the Grip Number, is associated with a reduction in crash frequency while an increase in roughness, as measured by the International Roughness Index, is associated with an increase in crash frequency. Thus, both pavement maintenance treatments aimed at increasing friction as well as treatments aimed at reducing irregularities have a positive safety effect, especially when wet, run-off-the-road or nighttime crashes are overrepresented. Study results allow to effectively integrate pavement management systems and safety management systems. When developing paving schedules, transportation agencies often base their decisions on asset management condition targets but do not explicitly account for the role of pavement conditions in highway safety. Availability of CMFs for both pavement surface parameters as well as geometric design parameters is crucial to improve pavement and geometric characteristics considering their safety effects.

Global simulations of galactic discs: violent feedback from clustered supernovae during bursts of star formation

ABSTRACT A suite of idealized, global, gravitationally unstable, star-forming galactic disc simulations with 2 pc spatial resolution, performed with the adaptive mesh refinement code ramses, is used in this paper to predict the emergent effects of supernova feedback. The simulations include a simplified prescription for the formation of single stellar populations of mass $\sim 100 \, {\rm M}_{\odot }$, radiative cooling, photoelectric heating, an external gravitational potential for a dark matter halo and an old stellar disc, self-gravity, and a novel implementation of supernova feedback. The results of these simulations show that gravitationally unstable discs can generate violent supersonic winds with mass-loading factors η ≳ 10, followed by a galactic fountain phase. These violent winds are generated by highly clustered supernovae exploding in dense environments created by gravitational instability, and they are not produced in simulation without self-gravity. The violent winds significantly perturb the vertical structure of the disc, which is later re-established during the galactic fountain phase. Gas resettles into a quasi-steady, highly turbulent disc with volume-weighted velocity dispersion $\sigma \gt 50 \, {\rm km\, s}^{-1}$. The new configuration drives weaker galactic winds with a mass-loading factor η ≤ 0.1. The whole cycle takes place in ≤10 dynamical times. Such high time variability needs to be taken into account when interpreting observations of galactic winds from starburst and post-starburst galaxies.

Amplitude-frequency characteristics analysis for vertical vibration of hydraulic AGC system under nonlinear action

Hydraulic automatic gauge control (AGC) system is the core control system to ensure the thickness accuracy of cold-rolled plate, and the reliability of its load roll system is the key to guarantee the rolling process with high precision, high speed, continuity and stability. However, the working mechanism of hydraulic AGC system is complex. And it possesses some features such as high nonlinearity, time variability and strong coupling. The vertical vibration easily happens in its working process. Moreover, the stability of system is seriously affected. Nevertheless, the incentive is difficult to determine. In this paper, the theory and method of nonlinear dynamics were introduced to establish the load vertical vibration (LVV) model of HAGC system under nonlinear action. The model was solved with multi-scale method, and the amplitude-frequency characteristic equation of system was obtained. Moreover, the research object of this article also focused on the nonlinear elastic force, nonlinear damping force and nonlinear excitation force, and deeply explored the effect of nonlinear parameters change on the amplitude-frequency characteristics of LVV system. The mechanism and incentive for LVV of hydraulic AGC system were further revealed. The research results lay a theoretical foundation for the vibration inducement and suppression of hydraulic AGC system.

Optimizing Lot Priority Rules in Semiconductor Production Systems

Production processes in the semiconductor industry (e.g. wafer production) are very complex. They are characterized by a high product variety, low-volume production, high cycle time variability and diversity in lot priorities. Low prices and low profit margins lead to immense competitive pressure and semiconductor manufacturers have to bring their products to the market with short lead times according to their contractual agreements. To meet customer requirements in terms of quantity, cost and lead time, high priority lots are pushed through the factory as quickly as possible. This prioritization of certain lots affects the other lots in the line. This results in longer lead times for the non-priority lots and may require additional setup operations. We build a queueing network model to model the manufacturing system under study and develop a hybrid decomposition method to analyse it. We optimize diverse configurations of the system under different contractual agreements regarding lead times/penalty costs.

Traffic and Transit Travel Time Reliability Indexes and Confidence Intervals

As congestion worsens, the importance of rigorous methodologies to estimate travel time reliability increases. Exploiting fine-granularity transit GPS data, this research proposes a novel method to estimate travel time percentiles and confidence intervals. Novel transit reliability measures based on travel time percentiles are proposed to identify and rank low-performance hot spots; the proposed reliability measures can be utilized to distinguish peak-hour low performance from whole-day low performance. As a case study, the methodology is applied to a bus transit corridor in Portland, Oregon. Time–space speed profiles, heat maps, and visualizations are employed to highlight sections and intersections with high travel time variability and low transit performance. Segment and intersection travel time reliability are contrasted against analytical delay formulas at intersections—with positive results. If bus stop delays are removed, this methodology can also be applied to estimate regular traffic travel time variability.

Analytical solution for nonlinear vertical vibration model of mill roll system based on improved complexification averaging method

Rolling mill is the core equipment in modern iron and steel industry, and the reliability of its mill roll system (MRS) is the key to ensure the rolling process with high precision, high speed, continuity and stability. However, the MRS possesses some features such as high nonlinearity, time variability and strong coupling. The vertical vibration easily happens in its working process. Nevertheless, the mathematical model of MRS is difficult to be established and hard to be solved. In this paper, the nonlinear dynamics theory and modern signal processing method were introduced to solve this difficult problem. A two degree of freedom (DOF) nonlinear vertical vibration model of MRS was established. And the model was analytically solved by using complexification averaging (CA) method. The solution error of CA method was thoroughly analyzed. Moreover, the CA method was improved by combining the fast empirical mode decomposition (FEMD) method. Research results indicate that the improved CA method presents a significant advantage in improving the solution precision, and can be used to solve strong nonlinear vibration system (NVS) with two DOF.

Search for muon-neutrino emission from GeV and TeV gamma-ray flaring blazars using five years of data of the ANTARES telescope

The ANTARES telescope is well-suited for detecting astrophysical transient neutrino sources as it can observe a full hemisphere of the sky at all times with a high duty cycle. The background due to atmospheric particles can be drastically reduced, and the point-source sensitivity improved, by selecting a narrow time window around possible neutrino production periods. Blazars, being radio-loud active galactic nuclei with their jets pointing almost directly towards the observer, are particularly attractive potential neutrino point sources, since they are among the most likely sources of the very high-energy cosmic rays. Neutrinos and gamma rays may be produced in hadronic interactions with the surrounding medium. Moreover, blazars generally show high time variability in their light curves at different wavelengths and on various time scales. This paper presents a time-dependent analysis applied to a selection of flaring gamma-ray blazars observed by the FERMI/LAT experiment and by TeV Cherenkov telescopes using five years of ANTARES data taken from 2008 to 2012. The results are compatible with fluctuations of the background. Upper limits on the neutrino fluence have been produced and compared to the measured gamma-ray spectral energy distribution.