Number Of Time Scales Research Articles

The (In)flexible self: Psychopathology, mindfulness, and neuroscience

Clinical and neuroscientific evidence indicates that transdiagnostic processes contribute to the generation and maintenance of psychopathological symptoms and disorders. Rigidity (inflexibility) appears a core feature of most transdiagnostic pathological processes. Decreasing rigidity may prove important to restore and maintain mental health. One of the primary domains in which rigidity and flexibility plays a role concerns the self. We adopt the pattern theory of self (PTS) for a working definition of self. This incorporates the pluralist view on self as constituted by multiple aspects or processes, understood to constitute a self-pattern, i.e. processes organized in non-linear dynamical relations across a number of time scales. The use of mindfulness meditation in the format of Mindfulness Based Interventions (MBIs) has been developed over four decades in Clinical Psychology. MBIs are promising as evidence-based treatments, shown to be equivalent to gold-standard treatments and superior to specific active controls in several randomized controlled trials. Notably, MBIs have been shown to target transdiagnostic symptoms. Given the hypothesized central role of rigid, habitual self-patterns in psychopathology, PTS offers a useful frame to understand how mindfulness may be beneficial in decreasing inflexibility. We discuss the evidence that mindfulness can alter the psychological and behavioral expression of individual aspects of the self-pattern, as well as favour change in the self-pattern as a whole gestalt.We discuss neuroscientific research on how the phenomenology of the self (pattern) is reflected in associated cortical networks and meditation-related alterations in cortical networks. Creating a synergy between these two aspects can increase understanding of psychopathological processes and improve diagnostic and therapeutic options.

Coupled modeling of peatlands carbon cycle and carbon dioxide emission from their peat deposits

COmplex Model of BOg LAndscapes (COMBOLA) is a set of dynamic models of carbon and nitrogen turnover, net ecosystem exchange, water balance, heat and water transport, generation and transfer of CO2 and CH4 in a peat deposit on a number of time scales. Combined modeling of carbon turnover and carbon dioxide emission from peat deposits on an annual time scale is carried out as part of COMBOLA for several bog landscapes in Western-Siberian south taiga. One-dimensional vertical reaction-diffusion model for carbon dioxide emission from peat deposit is combined with compartment carbon cycle model allowing estimate both bog type vegetation changes and greenhouse gases balance under probable climate change or perturbations initiated by human activities. Water table level (WTL) separating aerobic peat layer from the anaerobic one is calculated by means of the whole peat layer depth and its water balance relations. Minimal statistical information is used for calibrating temperature dependencies together with coefficients of heat and water transfer equations. The stationary states of carbon cycle are accompanied by steady average profiles of carbon dioxide concentration and can loose stability under human or climatic perturbations. The model dynamics is simulated with several climatic projections of the IPSL atmospheric circulation model for 21st century with anthropogenic scenarios RCP-2.6 and RCP-8.5.

Multiple-Timescale Feature Learning Strategy for Valve Stiction Detection Based on Convolutional Neural Network

This article proposes a valve stiction detection strategy based on a convolutional neural network. Considering the commonly existing characteristics of industrial time-series signals, the strategy is developed to learn features on multiple timescales automatically. Unlike the traditional approaches using hand-crafted features, the proposed strategy can automatically learn representative features on the time-series data collected from industrial control loops. The strategy is composed of two complementary data conversion methods, a mixed feature learning stage and a fusion decision stage, and it has the following merits: 1) the interaction of different pairs of time series can be effectively captured; and 2) the whole process of feature learning is automatic, and no manual feature extraction is needed. The effectiveness of the proposed strategy is evaluated through the comprehensive data, including the International Stiction Data Base, and the real data collected from the real hardware experimental system and the industrial environment. Compared with four traditional methods and three deep-learning-based methods, the experimental results demonstrate that the proposed strategy outperforms the other methods. Besides performance evaluation, we give the implementation procedure of practical application of the proposed strategy and provide the detailed analysis from the perspective of the data conversion methods and the number of timescales.

Characteristic Chemical Time Scales for Reactive Flow Modeling

ABSTRACT The chemical time scale can be used to characterize a reactive system(’s behavior). In addition, various dimensionless numbers (e.g. Damköhler number) rely on a characteristic chemical time scale. The inverse eigenvalues of a system are regarded as the system’s time scales. This means, the number of time scales is equal the numbers of eigenvalues. A formulation for a single characteristic time scale is required for the system characterization and to calculate dimensionless numbers. Recently proposed modifications of the Eddy Dissipation Concept (a turbulence-chemistry interaction model) also incorporate the Damköhler number in their formulation. Besides accuracy, the numerical efficiency is important, since the chemical time scale needs to be computed in each cell at every time step. We present different chemical time scale definitions found in literature, evaluate them on simple test problems and use them for flame simulations in conjunction with the modified Eddy Dissipation Concept. For the simple test case, most formulations give satisfactory results. The complexity of the chemical reaction mechanism greatly impacts the calculated time scale values. Therefore, we suggest to use a simple global mechanism for the calculation of chemical time scales to ensure reproducibility and consistency of the results.

A distribution‐based method to gauge market liquidity through scale invariance between investment horizons

AbstractA nonparametric method is developed to detect self‐similarity among the rescaled distributions of the log‐price variations over a number of time scales. The procedure allows to test the statistical significance of the scaling exponent that possibly characterizes each pair of time scales and to analyze the link between self‐similarity and liquidity, the core assumption of the fractal market hypothesis. The method can support financial operators in the selection of the investment horizons as well as regulators in the adoption of guidelines to improve the stability of markets. The analysis performed on the S&P500 reveals a very complex, time‐changing scaling structure, which confirms the link between market liquidity and self‐similarity.

Gamma-Nets: Generalizing Value Estimation over Timescale

Temporal abstraction is a key requirement for agents making decisions over long time horizons—a fundamental challenge in reinforcement learning. There are many reasons why value estimates at multiple timescales might be useful; recent work has shown that value estimates at different time scales can be the basis for creating more advanced discounting functions and for driving representation learning. Further, predictions at many different timescales serve to broaden an agent's model of its environment. One predictive approach of interest within an online learning setting is general value function (GVFs), which represent models of an agent's world as a collection of predictive questions each defined by a policy, a signal to be predicted, and a prediction timescale. In this paper we present Γ-nets, a method for generalizing value function estimation over timescale, allowing a given GVF to be trained and queried for arbitrary timescales so as to greatly increase the predictive ability and scalability of a GVF-based model. The key to our approach is to use timescale as one of the value estimator's inputs. As a result, the prediction target for any timescale is available at every timestep and we are free to train on any number of timescales. We first provide two demonstrations by 1) predicting a square wave and 2) predicting sensorimotor signals on a robot arm using a linear function approximator. Next, we empirically evaluate Γ-nets in the deep reinforcement learning setting using policy evaluation on a set of Atari video games. Our results show that Γ-nets can be effective for predicting arbitrary timescales, with only a small cost in accuracy as compared to learning estimators for fixed timescales. Γ-nets provide a method for accurately and compactly making predictions at many timescales without requiring a priori knowledge of the task, making it a valuable contribution to ongoing work on model-based planning, representation learning, and lifelong learning algorithms.

Time variability of the Enceladus plumes: Orbital periods, decadal periods, and aperiodic change

The Enceladus plumes vary on a number of timescales. Tidal stresses as Enceladus revolves in its eccentric orbit lead to a periodic diurnal variation in the mass and velocity of solid particles in the plume. Tidal stresses associated with an orbital resonance with Dione lead to a periodic decadal variation. Aperiodic variations occur on time scales of months, and may be due to ice buildup and flow of the walls of the fissures that connect the ocean to the surface. We document these variations using all the relevant data taken by the ISS instrument from 2005 to 2017. Key questions now include how a 5% peak-to-peak variation in orbital eccentricity, which itself is only 0.0045, could lead to a 2-fold decadal variation in plume properties. Another question is how the plumes stay open if ice builds up every month and clogs the vents. Other questions include why the solid particles exit the vents several times slower than the gas, and why the speeds vary inversely with the mass of the plumes. The Cassini data are in, but the modeling has just begun.

Contrasting Photo-physiological Responses of the Haptophyte <em>Phaeocystis Antarctica</em> and the Diatom <em>Pseudonitzschia</em> sp. in the Ross Sea (Antarctica)

The Antarctic is a unique environment in which substantial variations in irradiance occur over a number of time scales, and as a result phytoplankton need to acclimate and adapt to these changes. We conducted field and laboratory manipulations in the Ross Sea, Antarctica to examine photophysiological differences between Phaeocystis antarctica and Pseudonitzschia sp. a diatom that commonly occurrs in the Ross Sea, since these are the two functional groups that dominate abundance and productivity. Both exhibited reduced quantum yields due to high irradiances. P. antarctica , a haptophyte, displays a distinct photophysiological response to irradiance when compared to diatoms. P. antarctica showed a rapid recovery from high light exposure, as indicated by the rapid return to initial, high quantum yields, in contrast to diatoms, which responded more slowly. Absorption cross sections were high in both forms, but those in P. antarctica were significantly higher. Both organisms recovered within 24 h to initial quantum yields, suggesting that high irradiance exposure does not have a permanent effect on these organisms. Among all micronutrient additions (iron, cobalt, zinc and vitamin B 12 ), only iron additions resulted in rapid impacts on quantum yields. Iron limitation also can result in reduced photosynthetic efficiency. Understanding these photophysiologial responses and the impact of oceanographic conditions provides constraints on modeling efforts of photosynthesis and primary productivity in the Antarctic.

Desert dust hazards: A global review

Dust storms originate in many of the world’s drylands and frequently present hazards to human society, both within the drylands themselves but also outside drylands due to long-range transport of aeolian sediments. Major sources of desert dust include the Sahara, the Middle East, central and eastern Asia, and parts of Australia, but dust-raising occurs all across the global drylands and, on occasion, beyond. Dust storms occur throughout the year and they vary in frequency and intensity over a number of timescales. Long-range transport of desert dust typically takes place along seasonal transport paths. Desert dust hazards are here reviewed according to the three phases of the wind erosion system: where dust is entrained, during the transport phase, and on deposition. This paper presents a synthesis of these hazards. It draws on empirical examples in physical geography, medical geology and geomorphology to discuss case studies from all over the world and in various fields. These include accelerated soil erosion in agricultural zones – where dust storms represent a severe form of accelerated soil erosion – the health effects of air pollution caused by desert aerosols via their physical, chemical and biological properties, transport accidents caused by poor visibility during desert dust events, and impacts on electricity generation and distribution. Given the importance of desert dust as a hazard to human societies, it is surprising to note that there have been relatively few attempts to assess their impact in economic terms. Existing studies in this regard are also reviewed, but the wide range of impacts discussed in this paper indicates that desert dust storms deserve more attention in this respect.

A method for estimation of parameters in a neural model with noisy measurements

In this article, we establish a method for the estimation of parameters of a three-dimensional Hindmarsh–Rose (HR) neural model under noisy environment. It has been assumed that all the parameters are unknown and are expressed as the time-varying sinusoidal functions for the membrane voltage recordings. We apply the method to present the stochastic nature of parameters, applied current and membrane voltage. The proposed method shows that the estimation procedure needs a large number of time scales for which the solution will be more accurate and it provides the relation between the parameters. The stochastic 3D HR model is used, and the mean and variances are calculated. Our analysis explains how the parameters are estimated and it helps us to select an optimal simulation procedure. The estimation procedure is also derived for a particular case when the parameters are constants instead of time-varying functions. This paper reports the application of estimation technique to experimental studies in neural computation.

Hierarchical Biomolecular Dynamics: Picosecond Hydrogen Bonding Regulates Microsecond Conformational Transitions.

Biomolecules exhibit structural dynamics on a number of time scales, including picosecond (ps) motions of a few atoms, nanosecond (ns) local conformational transitions, and microsecond (μs) global conformational rearrangements. Despite this substantial separation of time scales, fast and slow degrees of freedom appear to be coupled in a nonlinear manner; for example, there is theoretical and experimental evidence that fast structural fluctuations are required for slow functional motion to happen. To elucidate a microscopic mechanism of this multiscale behavior, Aib peptide is adopted as a simple model system. Combining extensive molecular dynamics simulations with principal component analysis techniques, a hierarchy of (at least) three tiers of the molecule's free energy landscape is discovered. They correspond to chiral left- to right-handed transitions of the entire peptide that happen on a μs time scale, conformational transitions of individual residues that take about 1 ns, and the opening and closing of structure-stabilizing hydrogen bonds that occur within tens of ps and are triggered by sub-ps structural fluctuations. Providing a simple mechanism of hierarchical dynamics, fast hydrogen bond dynamics is found to be a prerequisite for the ns local conformational transitions, which in turn are a prerequisite for the slow global conformational rearrangement of the peptide. As a consequence of the hierarchical coupling, the various processes exhibit a similar temperature behavior which may be interpreted as a dynamic transition.

Structure and Dynamics of ER: Minimal Networks and Biophysical Constraints

The endoplasmic reticulum (ER) in live cells is a highly mobile network whose structure dynamically changes on a number of timescales. The role of such drastic changes in any system is unclear, although there are correlations with ER function. A better understanding of the fundamental biophysical constraints on the system will allow biologists to determine the effects of molecular factors on ER dynamics. Previous studies have identified potential static elements that the ER may remodel around. Here, we use these structural elements to assess biophysical principles behind the network dynamics. By analyzing imaging data of tobacco leaf epidermal cells under two different conditions, i.e., native state (control) and latrunculin B (treated), we show that the geometric structure and dynamics of ER networks can be understood in terms of minimal networks. Our results show that the ER network is well modeled as a locally minimal-length network between the static elements that potentially anchor the ER to the cell cortex over longer timescales; this network is perturbed by a mixture of random and deterministic forces. The network need not have globally minimum length; we observe cases where the local topology may change dynamically between different Euclidean Steiner network topologies. The networks in the treated cells are easier to quantify, because they are less dynamic (the treatment suppresses actin dynamics), but the same general features are found in control cells. Using a Langevin approach, we model the dynamics of the nonpersistent nodes and use this to show that the images can be used to estimate both local viscoelastic behavior of the cytoplasm and filament tension in the ER network. This means we can explain several aspects of the ER geometry in terms of biophysical principles.

Paired Porites coral Sr/Ca and δ18O from the western South China Sea: Proxy calibration of sea surface temperature and precipitation

Paired strontium-to-calcium (Sr/Ca) and δ18O measurements for two Porites lutea corals recovered from Hon Tre Island, Vietnam, are strongly correlated to sea surface temperature (SST) and precipitation at monthly to interannual time-scales. Least squares linear regression of monthly Sr/Ca to SST shows a strong, significant correlation (r2=0.77, p<.0001), with root mean square residuals of 0.9°C. 3-year averaged (binned) Sr/Ca for wet (Sep–Nov) and dry (Jan–Mar) seasons separately captures SST variability at interannual time scales (Sr/Ca RMSR=0.42°C and 0.70°C for wet and dry seasons, respectively). Coral δ18O correlates weakly to SST at seasonal and interannual time scales for wet and dry seasons, with significant anomalies (δ18O RMSR=2.4°C and 1.65°C, respectively). Correcting the SST influence on coral δ18O using paired Sr/Ca values provides estimates of δ18O of seawater (δ18Osw). 3-year averaged δ18Osw during the wet season shows a significant correlation to local precipitation (r2=0.54, p=0.01). These results show that coral Sr/Ca in this location accurately reflects SST at a number of timescales, and that seawater δ18O composition in the wet season is controlled by local precipitation, largely unmodified by ocean circulation during the winter monsoon. This study highlights the sensitivity and utility of coral geochemistry in this region for reliably reconstructing SST and monsoonal precipitation.

Addressing drought conditions under current and future climates in the Jordan River region

Abstract. The Standardized Precipitation–Evaporation Index (SPEI) was applied in order to address the drought conditions under current and future climates in the Jordan River region located in the southeastern Mediterranean area. In the first step, the SPEI was derived from spatially interpolated monthly precipitation and temperature data at multiple timescales: accumulated precipitation and monthly mean temperature were considered over a number of timescales – for example 1, 3, and 6 months. To investigate the performance of the drought index, correlation analyses were conducted with simulated soil moisture and the Normalized Difference Vegetation Index (NDVI) obtained from remote sensing. A comparison with the Standardized Precipitation Index (SPI), i.e., a drought index that does not incorporate temperature, was also conducted. The results show that the 6-month SPEI has the highest correlation with simulated soil moisture and best explains the interannual variation of the monthly NDVI. Hence, a timescale of 6 months is the most appropriate when addressing vegetation growth in the semi-arid region. In the second step, the 6-month SPEI was derived from three climate projections based on the Intergovernmental Panel on Climate Change emission scenario A1B. When comparing the period 2031–2060 with 1961–1990, it is shown that the percentage of time with moderate, severe and extreme drought conditions is projected to increase strongly. To address the impact of drought on the agricultural sector, the irrigation water demand during certain drought years was thereafter simulated with a hydrological model on a spatial resolution of 1 km. A large increase in the demand for irrigation water was simulated, showing that the agricultural sector is expected to become even more vulnerable to drought in the future.

Natural and anthropogenic variations in methane sources during the past two millennia

Methane is an important greenhouse gas that is emitted from multiple natural and anthropogenic sources. Atmospheric methane concentrations have varied on a number of timescales in the past, but what has caused these variations is not always well understood. The different sources and sinks of methane have specific isotopic signatures, and the isotopic composition of methane can therefore help to identify the environmental drivers of variations in atmospheric methane concentrations. Here we present high-resolution carbon isotope data (δ(13)C content) for methane from two ice cores from Greenland for the past two millennia. We find that the δ(13)C content underwent pronounced centennial-scale variations between 100 BC and AD 1600. With the help of two-box model calculations, we show that the centennial-scale variations in isotope ratios can be attributed to changes in pyrogenic and biogenic sources. We find correlations between these source changes and both natural climate variability--such as the Medieval Climate Anomaly and the Little Ice Age--and changes in human population and land use, such as the decline of the Roman empire and the Han dynasty, and the population expansion during the medieval period.

The role of cladocerans in tracking long-term change in shallow lake trophic status

Shallow lakes have been affected by a variety of human activities profoundly altering their ecological structure and function. Cladocerans have been used to track change resulting from a variety of drivers at a number of time scales. Aquatic macrophytes are well recognised as reflecting the ecological condition of a lake. Here, we compare the plant macrofossils with the sub-fossil cladoceran assemblages from 20 dated sediment cores. Co-correspondence analysis was used to determine the degree of commonality of change in community composition of the two biological groups through time. This analysis revealed very high levels of agreement in the nature and timing of change at all the sites examined with very high correlation coefficients between the axis 1 scores for macrofossils and cladocerans. Furthermore, at all sites a high proportion of the variance (min 20%, max 54%) in the macrofossil data was explained by the change in the cladoceran assemblage. Sub-fossil macrofossil and cladoceran assemblages, from at least from 1700 AD onwards, were examined in more detail at three sites: Ormesby Great Broad, Felbrigg Lake and Lake Sobygaard. There was very good accord in the main shifts of the cladoceran and macrofossil assemblages at all three sites. This may reflect the long-term shift in the principal focus of primary production from the benthic to the pelagic habitat. We suggest that the combination of their central position in the food-web and the presence of both pelagic and benthic taxa make cladocerans a strong candidate as the single best indicator of (palaeo) ecological condition related to changing trophic status and alteration in food-web structure in shallow lakes.

Hearing It Again and Again: On-Line Subcortical Plasticity in Humans

BackgroundHuman brainstem activity is sensitive to local sound statistics, as reflected in an enhanced response in repetitive compared to pseudo-random stimulus conditions [1]. Here we probed the short-term time course of this enhancement using a paradigm that assessed how the local sound statistics (i.e., repetition within a five-note melody) interact with more global statistics (i.e., repetition of the melody).Methodology/Principal FindingsTo test the hypothesis that subcortical repetition enhancement builds over time, we recorded auditory brainstem responses in young adults to a five-note melody containing a repeated note, and monitored how the response changed over the course of 1.5 hrs. By comparing response amplitudes over time, we found a robust time-dependent enhancement to the locally repeating note that was superimposed on a weaker enhancement of the globally repeating pattern.Conclusions/SignificanceWe provide the first demonstration of on-line subcortical plasticity in humans. This complements previous findings that experience-dependent subcortical plasticity can occur on a number of time scales, including life-long experiences with music and language, and short-term auditory training. Our results suggest that the incoming stimulus stream is constantly being monitored, even when the stimulus is physically invariant and attention is directed elsewhere, to augment the neural response to the most statistically salient features of the ongoing stimulus stream. These real-time transformations, which may subserve humans' strong disposition for grouping auditory objects, likely reflect a mix of local processes and corticofugal modulation arising from statistical regularities and the influences of expectation. Our results contribute to our understanding of the biological basis of statistical learning and initiate a new investigational approach relating to the time-course of subcortical plasticity. Although the reported time-dependent enhancements are believed to reflect universal neurophysiological processes, future experiments utilizing a larger array of stimuli are needed to establish the generalizability of our findings.

Auditory Brain Stem Response to Complex Sounds: A Tutorial

This tutorial provides a comprehensive overview of the methodological approach to collecting and analyzing auditory brain stem responses to complex sounds (cABRs). cABRs provide a window into how behaviorally relevant sounds such as speech and music are processed in the brain. Because temporal and spectral characteristics of sounds are preserved in this subcortical response, cABRs can be used to assess specific impairments and enhancements in auditory processing. Notably, subcortical auditory function is neither passive nor hardwired but dynamically interacts with higher-level cognitive processes to refine how sounds are transcribed into neural code. This experience-dependent plasticity, which can occur on a number of time scales (e.g., life-long experience with speech or music, short-term auditory training, on-line auditory processing), helps shape sensory perception. Thus, by being an objective and noninvasive means for examining cognitive function and experience-dependent processes in sensory activity, cABRs have considerable utility in the study of populations where auditory function is of interest (e.g., auditory experts such as musicians, and persons with hearing loss, auditory processing, and language disorders). This tutorial is intended for clinicians and researchers seeking to integrate cABRs into their clinical or research programs.

Transient behavior of the planar-flow melt spinning process

Planar-flow melt spinning (PFMS) is a single-stage rapid manufacturing technique for producing thin metal sheets or ribbons. Commercial acceptance of PFMS requires ribbons to be cast with uniform thickness to tolerance. The process feeds molten metal through a nozzle onto a moving wheel where a puddle is formed and from which a continuous ribbon is ‘spun’. This study focuses on the time dependent behavior of the process. The dynamics of the process are modeled using unsteady mass balances, combined with a Bernoulli model of flow. Variations over a number of time scales are observed. The time scale over which the process can be treated as ‘quasi-steady’ is identified. Measuring the evolution of the puddle length and ribbon thickness within a cast indicates that the solidification rate also varies with time and a maximum in solidification rate is inferred.

Submonthly Indian Ocean Cooling Events and Their Interaction with Large-Scale Conditions

Abstract The Indian Ocean exhibits strong variability on a number of time scales, including prominent intraseasonal variations in both the atmosphere and ocean. Of particular interest is the south tropical Indian Ocean thermocline ridge, a region located between 12° and 5°S, which exhibits prominent variability in sea surface temperature (SST) due to dominant winds that raise the thermocline and shoal the mixed layer. In this paper, submonthly (less than 30 day) cooling events in the thermocline ridge region are diagnosed with observations and models, and are related to large-scale conditions in the Indo-Pacific region. Observations from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) satellite were used to identify 16 cooling events in the period 1998–2007, which on average cannot be fully accounted for by air–sea enthalpy fluxes. Analysis of observations and a hierarchy of models, including two coupled global climate models (GFDL CM2.1 and GFDL CM2.4), indicates that ocean dynamical changes are important to the cooling events. For extreme cooling events (above 2.5 standard deviations), air–sea enthalpy fluxes account for approximately 50% of the SST signature, and oceanic processes cannot in general be neglected. For weaker cooling events (1.5–2.5 standard deviations), air–sea enthalpy fluxes account for a larger fraction of the SST signature. Furthermore, it is found that cooling events are preconditioned by large-scale, low-frequency changes in the coupled ocean–atmosphere system. When the thermocline is unusually shallow in the thermocline ridge region, cooling events are more likely to occur and are stronger; these large-scale conditions are more (less) likely during La Niña (El Niño/Indian Ocean dipole) events. Strong cooling events are associated with changes in atmospheric convection, which resemble the Madden–Julian oscillation, in both observations and the models.