Cyclic Dynamics Research Articles

Gravitational attraction until relativistic equipartition of internal and translational kinetic energies

Translational ordering of the internal kinematic chaos provides the Special Relativity referents for the geodesic motion of warm thermodynamical bodies. Taking identical mathematics, relativistic physics of the low speed transport of time-varying heat-energies differs from Newton's physics of steady masses without internal degrees of freedom. General Relativity predicts geodesic changes of the internal heat-energy variable under the free gravitational fall and the geodesic turn in the radial field center. Internal heat variations enable cyclic dynamics of decelerated falls and accelerated takeoffs of inertial matter and its structural self-organization. The coordinate speed of the ordered spatial motion takes maximum under the equipartition of relativistic internal and translational kinetic energies. Observable predictions are discussed for verification/falsification of the principle of equipartition as a new basic for the ordered motion and self-organization in external fields, including gravitational, electromagnetic, and thermal ones.

A Macroeconomic Approach to the Term Premium

In recent years, term premia have been very low and sometimes even negative. Now, with the United States economy growing above potential, inflationary pressures are on the rise. Term premia are very sensitive to the expected future path of growth, inflation, and monetary policy, and an inflation surprise could require monetary policy to tighten faster than anticipated, inducing to a sudden decompression of term and other risk premia, thus tightening financial conditions. This paper proposes a semi-structural dynamic term structure model augmented with macroeconomic factors to include cyclical dynamics with a focus on medium- to long-run forecasts. Our results clearly show that a macroeconomic approach is warranted: While term premium estimates are in line with those from other studies, we provide (i) plausible, stable estimates of expected long-term interest rates and (ii) forecasts of short- and long-term interest rates as well as cyclical macroeconomic variables that are stunningly close to those generated from large-scale macroeconomic models.

Persistence and Cyclical Dynamics of U.S. and U.K. House Prices: Evidence from Over 150 Years of Data

This paper provides a new and unique look at the dynamics and persistence of historical house prices in the US and the UK using fractional integration techniques not previously applied to housing markets. We use annual data from 1830 to 2016 for the US and 1845 to 2016 for the UK, which provides a much longer perspective on the behavior of house prices than is common in the literature, where most empirical work uses data starting from the 1980s or later. Unlike previous research, we consider two components of persistence of house prices: the component associated with the long-run trend (i.e. at frequency zero) and the component associated with the cycle (i.e. at frequency away from zero). We find convincing evidence that in the UK, housing markets incorporate two distinct poles in house price dynamics, at the zero (long-run trend) and non-zero (cyclical) frequencies. In contrast, we fail to find evidence of cyclical persistence for the US. For the sub-samples, which account for a structural break in each series, an important difference is the asynchronous pattern of the breaks, an indication of heterogeneity in the house price dynamics of the two countries and a sign that national rather than global events have played an important role. Although the house price movements of the last decade are dramatic, the greatest structural changes in the overall nominal and real price dynamics of the UK and the US appear to have taken place much earlier, in the late 1970s and early 1980s in the UK, and in the mid-1950s and early 1970s in the US. An important result, common to the whole and sub-samples, is that long-run persistence plays a greater role than cyclical persistence in explaining the dynamics of house prices in both countries. These findings have substantial implications for policy decisions. Shocks affecting the long-run component will persist for a long time, while those affecting the cyclical component will not. Thus, policymakers should adopt stronger policies with respect to long-run house price movements to create an environment whereby housing markets can readily revert to their original trends.

The Ins and Outs of Involuntary Part-time Employment

We develop and implement a protocol to measure U.S. monthly time series of involuntary part-time employment stocks and flows from 1976 until today. Armed with these new data, we provide a comprehensive account of the cyclical dynamics of involuntary part-time work. We find that the recessionary increase in involuntary part-time employment is consistently driven by a jump in the transition probability from other employment states to involuntary part-time employment, and a drop in the reverse transition probability. We compare the dynamics of unemployment and involuntary part-time employment to argue that they reflect the operation of distinct labor-adjustment channels. While unemployment dynamics are driven by movements in job creation and destruction, the dynamics of involuntary part-time employment reflect changes in employers' labor utilization.

A Macroeconomic Approach to the Term Premium

In recent years, term premia have been very low and sometimes even negative. Now, with the United States economy growing above potential, inflationary pressures are on the rise. Term premia are very sensitive to the expected future path of growth, inflation, and monetary policy, and an inflation surprise could require monetary policy to tighten faster than anticipated, inducing to a sudden decompression of term and other risk premia, thus tightening financial conditions. This paper proposes a semi-structural dynamic term structure model augmented with macroeconomic factors to include cyclical dynamics with a focus on medium- to long-run forecasts. Our results clearly show that a macroeconomic approach is warranted: While term premium estimates are in line with those from other studies, we provide (i) plausible, stable estimates of expected long-term interest rates and (ii) forecasts of short- and long-term interest rates as well as cyclical macroeconomic variables that are stunningly close to those generated from large-scale macroeconomic models.

The dynamics of Kelp Forests in the Northeast Pacific Ocean and the relationship with environmental drivers

Abstract The dynamics of foundation species in ecosystems are key to the fate of many species. Kelp forests are foundation species in temperate ocean ecosystems and contribute to carbon storage, macronutrient dynamics, primary production and biodiversity of a myriad of associated species. Downward trends in their abundance globally have been of concern. We analysed 26 years of aerial censuses (1989–2015) of two canopy kelp species in Washington State (USA) waters. We compared these modern censuses with censuses in 1911 and 1912 to determine the persistence of kelp cover over the past century. Using Autoregressive Integrated Moving Average (ARIMA) models, we compared kelp dynamics with likely environmental drivers, including local environmental variables and ocean indices for this region. Kelp remains at historic levels in many areas, although some eastern populations in proximity to greater human populations are the exception to this pattern. Over the last 26 years, kelp abundance showed high spatial autocorrelation in western areas of Straits of Juan de Fuca, with more variable populations in the annual species and eastward towards Puget Sound. Both species covaried positively in their abundance throughout most of the study area, suggesting that environmental factors rather than competition, drove their dynamics. The population dynamics of these kelp species showed that the abundance 1 year previously was an important predictor, and cyclic dynamics were not indicated using ARIMA models. Kelp abundance correlated inversely with the Pacific Decadal Oscillation and the Oceanic Nino Index, and positively with the North Pacific Gyre Oscillation, indicating that large‐scale processes associated with colder seawater temperatures were associated with greater relative abundance of kelp. Synthesis. Kelp beds in the northern California Current Large Marine Ecosystem have mostly remained persistent over the past century and over many kilometres, but some areas may have decreased in abundance. The sensitivity of these populations to indices of ocean climate, our demonstration that a historic 93‐year sea surface temperature record (Race Rocks, Canada) showed a 0.72°C increase and the classification of some areas as high variability‐low abundance, suggest that the viability of these foundational species remain a concern into the future.

Dynamical Behaviors in Complex-Valued Love Model With or Without Time Delays

In this paper, a novel version of nonlinear model, i.e. a complex-valued love model with two time delays between two individuals in a love affair, has been proposed. A notable feature in this model is that we separate the emotion of one individual into real and imaginary parts to represent the variation and complexity of psychophysiological emotion in romantic relationship instead of just real domain, and make our model much closer to reality. This is because love is a complicated cognitive and social phenomenon, full of complexity, diversity and unpredictability, which refers to the coexistence of different aspects of feelings, states and attitudes ranging from joy and trust to sadness and disgust. By analyzing associated characteristic equation of linearized equations for our model, it is found that the Hopf bifurcation occurs when the sum of time delays passes through a sequence of critical value. Stability of bifurcating cyclic love dynamics is also derived by applying the normal form theory and the center manifold theorem. In addition, it is also shown that, for some appropriate chosen parameters, chaotic behaviors can appear even without time delay.

Geographical Network Analysis and Spatial Econometrics as Tools to Enhance Our Understanding of Student Migration Patterns and Benefits in the U.S. Higher Education Network

This study measures the extent to which student outmigration outside the 4-year sector takes place and posits that the benefits from attracting non-resident students exist regardless of sector of enrollment. The study also provides empirical evidence about the relevance of employing geographical network analysis (GNA) and spatial econometrics in [higher] education research. When applied to student migration, GNA enabled the visualization and analysis of complex (virtuous cyclical) dynamics in non-resident student exchanges. Findings revealed that states’ preponderance in attracting non-resident students is both explained by their propensity to export their resident students out-of-state while it simultaneously affected non-resident tuition variation.

Weather-driven change in primary productivity explains variation in the amplitude of two herbivore population cycles in a boreal system.

Vertebrate populations throughout the circumpolar north often exhibit cyclic dynamics, and predation is generally considered to be a primary driver of these cycles in a variety of herbivore species. However, weather and climate play a role in entraining cycles over broad landscapes and may alter cyclic dynamics, although the mechanism by which these processes operate is uncertain. Experimental and observational work has suggested that weather influences primary productivity over multi-year time periods, suggesting a pathway through which weather and climate may influence cyclic herbivore dynamics. Using long-term monitoring data, we investigated the relationships among multi-year weather conditions, measures of primary productivity, and the abundance of two cyclic herbivore species: snowshoe hare and northern red-backed vole. We found that precipitation (rain and snow) and growing season temperatures were strongly associated with variation in primary productivity over multi-year time horizons. In turn, fourfold variation in the amplitude of both the hare and vole cycles observed in our study area corresponded to long-term changes in primary productivity. The congruence of our results for these two species suggests a general mechanism by which weather and climate might influence cyclic herbivore population dynamics. Our findings also suggested that the association between climate warming and the disappearance of cycles might be initiated by changes in primary productivity. This work provides an explanation for observed influences of weather and climate on primary productivity and population cycles and will help our collective understanding of how future climate warming may influence these ecological phenomena in the future.

A New Framework for Analysis of Coevolutionary Systems—Directed Graph Representation and Random Walks

Studying coevolutionary systems in the context of simplified models (i.e., games with pairwise interactions between coevolving solutions modeled as self plays) remains an open challenge since the rich underlying structures associated with pairwise-comparison-based fitness measures are often not taken fully into account. Although cyclic dynamics have been demonstrated in several contexts (such as intransitivity in coevolutionary problems), there is no complete characterization of cycle structures and their effects on coevolutionary search. We develop a new framework to address this issue. At the core of our approach is the directed graph (digraph) representation of coevolutionary problems that fully captures structures in the relations between candidate solutions. Coevolutionary processes are modeled as a specific type of Markov chains-random walks on digraphs. Using this framework, we show that coevolutionary problems admit a qualitative characterization: a coevolutionary problem is either solvable (there is a subset of solutions that dominates the remaining candidate solutions) or not. This has an implication on coevolutionary search. We further develop our framework that provides the means to construct quantitative tools for analysis of coevolutionary processes and demonstrate their applications through case studies. We show that coevolution of solvable problems corresponds to an absorbing Markov chain for which we can compute the expected hitting time of the absorbing class. Otherwise, coevolution will cycle indefinitely and the quantity of interest will be the limiting invariant distribution of the Markov chain. We also provide an index for characterizing complexity in coevolutionary problems and show how they can be generated in a controlled manner.

A method to predict fatigue crack initiation in metals using dislocation dynamics

AbstractA theory is proposed to predict the initiation of fatigue cracks using cyclic dislocation dynamics (DD) simulations. The evolution of dislocation networks in a grain is simulated over several cycles. It is shown that the dislocation density and the energy stored in the dislocation networks increase with the number of cycles. The results of the DD simulations are used to construct an energy balance expression for crack initiation. A hypothetical crack is inserted into the grain, and the Gibbs energy consisting of the energy of the dislocation structure, the surface energy of the hypothetical crack, and the reduction in continuum energy is evaluated. Once the Gibbs energy attains a maximum, the dislocation structure becomes unstable, and it becomes energetically more favorable to form a real crack. The proposed method is applied to oxygen-free high conductivity copper, and the results are compared against experiments. Finally, it is shown how the method can be amended to account for environmental effects.

Spatial and temporal variation in the range-wide cyclic dynamics of greater sage-grouse.

Periodic changes in abundance, or population cycles, are common in a variety of species and is one of the most widely studied ecological phenomena. The strength of, and synchrony between population cycles can vary across time and space and understanding these patterns can provide insight into the mechanisms generating population cycles and their variability within and among species. Here, we used wavelet and spectral analysis on a range-wide dataset of abundance for the greater sage-grouse (Centrocercus urophasianus) to test for regional differences in temporal cyclicity. Overall, we found that most populations (11 of 15) were cyclic at some point in a 50-year time series (1965-2015), but the patterns varied over both time and space. Several peripheral populations demonstrated amplitude dampening or loss of cyclicity following population lows in the mid-1990s. Populations through the core of the range in the Great and Wyoming Basins had more consistent cyclic dynamics, but period length appeared to shorten from 10-12 to 6-8years. In one time period, where cyclicity was greatest overall, increased pairwise population synchrony was correlated with cycle intensity. Our work represents a comprehensive range-wide assessment of cyclic dynamics and revealed substantial variation in temporal and spatial trends of cyclic dynamics across populations.

Technologies and problems of reengineering of the business processes of company

Management of the combination of business processes is a modern approach in the field of business management. Together with a lot of management approaches business processes allow us to identify all the resultant actions. Article reveals the modern view on the essence of business processes as well as the general approaches of their allocation. Principles of construction and business process re-engineering are proposed. Recommendations on how to perform re-engineering under high cyclic dynamics of business activity are provided.

Population cycles emerging through multiple interaction types.

Cyclic dynamics of populations are outstanding and widespread phenomena across many taxa. Previous theoretical studies have mainly focused on the consumer–resource interaction as the driving force for such cycling. However, natural ecosystems comprise diverse types of species interactions, but their roles in population dynamics remains unclear. Here, using a four-species hybrid module with antagonistic, mutualistic and competitive interactions, we analytically showed that the system with major interaction types can drive population cycles. Stronger interactions easily cause cycling, and even when sub-modules with possible combinations of two interactions are stabilized by weak interactions, the system with all interaction types can cause unstable population oscillations. Diversity of interaction types allows to add mutualists to the list of drivers of oscillations in a focal species' population size, when they act in conjunction to other drivers.

Population extinctions can increase metapopulation persistence.

Metapopulations persist when local populations are rapidly recolonized following local extinctions. Such persistence requires asynchrony; simultaneous crashes of all populations would leave no source of recolonization. We show theoretically and experimentally that catastrophic population extinctions themselves can promote metapopulation persistence, by preventing spatial synchrony and thus enhancing recolonization. We refer to this behaviour as the 'spatial hydra effect': as with the mythical hydra that grows two new heads when one is removed, extinctions can increase recolonization. The effect is robust, occurring in a wide range of theoretical models exhibiting cyclic or quasi-cyclic population dynamics. In a laboratory microcosm experiment using cyclic protist predator-prey metapopulations, catastrophic perturbations wiping out populations but leaving the patch otherwise unchanged increased metapopulation persistence when high dispersal rates would otherwise have led to spatially synchronous extinctions of all populations. We discuss several candidate examples of the spatial hydra effect in nature.

Spatial disease dynamics of free-living pathogens under pathogen predation

The epidemiological dynamics of potentially free-living pathogens are often studied with respect to a specific pathogen species (e.g., cholera) and most studies concentrate only on host-pathogen interactions. Here we show that metacommunity-level interactions can alter conventional spatial disease dynamics. We introduce a pathogen eating consumer species and investigate a deterministic epidemiological model of two habitat patches, where both patches can be occupied by hosts, pathogens, and consumers of free-living pathogens. An isolated habitat patch shows periodic disease outbreaks in the host population, arising from cyclic consumer-pathogen dynamics. On the other hand, consumer dispersal between the patches generate asymmetric disease prevalence, such that the host population in one patch stays disease-free, while disease outbreaks occur in the other patch. Such asymmetry can also arise with host dispersal, where infected hosts carry pathogens to the other patch. This indirect movement of pathogens causes also a counter-intuitive effect: decreasing morbidity in a focal patch under increasing pathogen immigration. Our results underline that community-level interactions influence disease dynamics and consistent spatial asymmetry can arise also in spatially homogeneous systems.

Period-doubling and Neimark–Sacker bifurcations in a larch budmoth population model

We investigate a discrete consumer-resource system based on a model originally proposed for studying the cyclic dynamics of the larch budmoth population in the Swiss Alps. It is shown that the moth population can persist indefinitely for all of the biologically feasible parameter values. Using intrinsic growth rate of the consumer population as a bifurcation parameter, we prove that the system can either undergo a period-doubling or a Neimark–Sacker bifurcation when the unique interior steady state loses its stability.

Interannual cycles of Hantaan virus outbreaks at the human–animal interface in Central China are controlled by temperature and rainfall

Hantavirus, a rodent-borne zoonotic pathogen, has a global distribution with 200,000 human infections diagnosed annually. In recent decades, repeated outbreaks of hantavirus infections have been reported in Eurasia and America. These outbreaks have led to public concern and an interest in understanding the underlying biological mechanisms. Here, we propose a climate-animal-Hantaan virus (HTNV) infection model to address this issue, using a unique dataset spanning a 54-y period (1960-2013). This dataset comes from Central China, a focal point for natural HTNV infection, and includes both field surveillance and an epidemiological record. We reveal that the 8-y cycle of HTNV outbreaks is driven by the confluence of the cyclic dynamics of striped field mouse (Apodemus agrarius) populations and climate variability, at both seasonal and interannual cycles. Two climatic variables play key roles in the ecology of the HTNV system: temperature and rainfall. These variables account for the dynamics in the host reservoir system and markedly affect both the rate of transmission and the potential risk of outbreaks. Our results suggest that outbreaks of HTNV infection occur only when climatic conditions are favorable for both rodent population growth and virus transmission. These findings improve our understanding of how climate drives the periodic reemergence of zoonotic disease outbreaks over long timescales.

The value of prior knowledge in machine learning of complex network systems.

Our overall goal is to develop machine-learning approaches based on genomics and other relevant accessible information for use in predicting how a patient will respond to a given proposed drug or treatment. Given the complexity of this problem, we begin by developing, testing and analyzing learning methods using data from simulated systems, which allows us access to a known ground truth. We examine the benefits of using prior system knowledge and investigate how learning accuracy depends on various system parameters as well as the amount of training data available. The simulations are based on Boolean networks-directed graphs with 0/1 node states and logical node update rules-which are the simplest computational systems that can mimic the dynamic behavior of cellular systems. Boolean networks can be generated and simulated at scale, have complex yet cyclical dynamics and as such provide a useful framework for developing machine-learning algorithms for modular and hierarchical networks such as biological systems in general and cancer in particular. We demonstrate that utilizing prior knowledge (in the form of network connectivity information), without detailed state equations, greatly increases the power of machine-learning algorithms to predict network steady-state node values ('phenotypes') and perturbation responses ('drug effects'). Links to codes and datasets here: https://gray.mgh.harvard.edu/people-directory/71-david-craft-phd. dcraft@broadinstitute.org. Supplementary data are available at Bioinformatics online.

Novel insight into the origin of the growth dynamics of sauropod dinosaurs.

Sauropod dinosaurs include the largest terrestrial animals and are considered to have uninterrupted rapid rates of growth, which differs from their more basal relatives, which have a slower cyclical growth. Here we examine the bone microstructure of several sauropodomorph dinosaurs, including basal taxa, as well as the more derived sauropods. Although our results agree that the plesiomorphic condition for Sauropodomorpha is cyclical growth dynamics, we found that the hypothesized dichotomy between the growth patterns of basal and more derived sauropodomorphs is not supported. Here, we show that sauropod-like growth dynamics of uninterrupted rapid growth also occurred in some basal sauropodomorphs, and that some basal sauropods retained the plesiomorphic cyclical growth patterns. Among the sauropodomorpha it appears that the basal taxa exploited different growth strategies, but the more derived Eusauropoda successfully utilized rapid, uninterrupted growth strategies.