Stationary Transition Research Articles

Spatial Imaging and Screening for Regime Shifts

Screening is a strategy for detecting undesirable change prior to manifestation of symptoms or adverse effects. Although the well-recognized utility of screening makes it commonplace in medicine, it has yet to be implemented in ecosystem management. Ecosystem management is in an era of diagnosis and treatment of undesirable change, and as a result, remains more reactive than proactive and unable to effectively deal with today’s plethora of non-stationary conditions. In this paper, we introduce spatial imaging-based screening to ecology. We link advancements in spatial resilience theory, data, and technological and computational capabilities and power to detect regime shifts (i.e., vegetation state transitions) that are known to be detrimental to human well-being and ecosystem service delivery. With a state-of-the-art landcover dataset and freely-available, cloud-based, geospatial computing platform, we screen for spatial signals of the three most iconic vegetation transitions studied in western USA rangelands: (1) erosion and desertification; (2) woody encroachment; and (3) annual exotic grass invasion. For a series of locations that differ in ecological complexity and geographic extent, we answer the following questions: (1) Which regime shift is expected or of greatest concern? (2) Can we detect a transition signal associated with the expected regime shift? (3) If detected, is the transition signal transient or persistent over time? (4) If detected and persistent, is the transition signal stationary or non-stationary over time? (5) What other signals do we detect? Our approach reveals a powerful and flexible methodology, whereby professionals can use spatial imaging to verify the occurrence of alternative vegetation regimes, image the spatial boundaries separating regimes, track the magnitude and direction of regime shift signals, differentiate persistent and stationary transition signals that warrant continued screening from the more concerning persistent and non-stationary transition signals, and then leverage disciplinary strength and resources for more targeted diagnostic testing (e.g., inventory and monitoring) and treatment (e.g., management) of regime shifts. While the rapid screening approach used here can continue to be implemented and refined for rangelands, it has broader implications and can be adapted to other ecological systems to revolutionize the information space needed to better manage critical transitions in nature.

Maximal Lyapunov Exponents and Steady-State Moments of a VI System based Upon TDFC and VED

This paper focuses on the investigation of a vibro-impact (VI) system based upon time-delayed feedback control (TDFC) and visco-elastic damping (VED) under bounded random excitations. A pretreatment for the TDFC and VED is necessary. A further simplification for the system is achieved by introducing the mirror image transformation. The averaging approach is adopted to analyze the above system relying on a parametric principal resonance consideration. By means of the first kind of a modified Bessel function, explicit asymptotic formulas for the maximal Lyapunov exponent (MLE) are given to examine the almost sure stability or instability of the trivial steady-state amplitude solution. Besides, the steady-state moments (SSM) of the nontrivial solutions of the system’s amplitude are derived by the application of the moment method and Itô’s calculus. Finally, the stability and its critical situations of the trivial solution are explored in detail through the important system parameters, i.e. embodying the TDFC parameters, the VED parameters, the restitution coefficient, the excitation amplitude and the random noise intensity. They are tested by numerical simulations. Additionally, the exploration of the steady-state moments involves the emergence of the general frequency response curve and the frequency island, discussions of conditions satisfied by the unstable boundary, and variations of the time-delayed island. Stochastic jumps and bifurcations are observed for the stationary joint transition probability density of the system’s trivial and nontrivial solutions based on parameter schemes of VED and TDFC.

Comparing Markov and non-Markov alternatives for cost-effectiveness analysis: Insights from a cervical cancer case

Markov model allows medical prognosis to be modeled with health state transitions over time and are particularly useful for decisions regarding diseases where uncertain events and outcomes may occur. To provide sufficient detail for operations researchers to carry out a Markov analysis, we present a detailed example of a Markov model with five health states with monthly transitions with stationary transition probabilities between states to model the cost and effectiveness of two treatments for advanced cervical cancer. A different approach uses survival curves to directly model the fraction of patients in each state at each time period without the Markov property. We use this alternative method to analyze the cervical cancer case and compare the Markov and non-Markov approaches. These models provide useful insights about both the effectiveness of treatments and the associated costs for healthcare decision makers.

Mechanism of Direct Electrophilic Aromatic Amination: an Electrophile is Found by Quantum‐Chemical Study

AbstractDirect amination is an extremely valuable reaction, allowing for the one‐step preparation of aromatic amines. However, its mechanism was poorly studied. Here, for the first time, using quantum chemical calculations, we have shown that direct amination of arenes by hydrazoic acid follows the classical SEAr mechanism with aminodiazonium cation H2N3+ as electrophile. The peculiarity of H2N3+ electronic structure has been described using our novel method for tracing the molecular orbitals. The located stationary points and transition states allowed us to define direct amination as SEAr reaction, which rate is determined by early transition state between π‐ and σ‐complexes. Considering the calculated reaction constant ρ and an early transition state, we placed direct amination of arenes by HN3 somewhere in‐between nitration and halogenations. Our results explain the accumulated experimental data and open a prospect for the development of the new aminating agents working in milder conditions.

Expression and regulation of the Pseudomonas aeruginosa hibernation promoting factor

Bacterial biofilms contain subpopulations of cells that are dormant and highly tolerant to antibiotics. While dormant, the bacteria must maintain the integrity of macromolecules required for resuscitation. Previously, we showed that hibernation promoting factor (HPF) is essential for protecting Pseudomonas aeruginosa from ribosomal loss during dormancy. In this study, we mapped the genetic components required for hpf expression. Using 5'-RACE and fluorescent protein reporter fusions, we show that hpf is expressed as part of the rpoN operon, but that hpf also has a second promoter (Phpf ) within the rpoN gene. Phpf is active when the cells enter stationary phase, and expression from Phpf is modulated, but not eliminated, in mutant strains impaired in stationary phase transition (ΔdksA2, ΔrpoS and ΔrelA/ΔspoT mutants). The results of reporter gene studies and mRNA folding predictions indicated that the 5' end of the hpf mRNA may also influence hpf expression. Mutations that opened or that stabilized the mRNA hairpin loop structures strongly influenced the amount of HPF produced. The results demonstrate that hpf is expressed independently of rpoN, and that hpf regulation includes both transcriptional and post-transcriptional processes, allowing the cells to produce sufficient HPF during stationary phase to maintain viability while dormant.

Accuracy between optical and inertial motion capture systems for assessing trunk speed during preferred gait and transition periods

Motion capture through inertial sensors is becoming popular, but its accuracy to describe kinematics during changes in walking speed is unknown. The aim of this study was to determine the accuracy of trunk speed extracted using an inertial motion system compared to a gold standard optical motion system, during steady walking and stationary periods. Eleven participants walked on pre-established paths marked on the floor. Between each lap, a 1-second stationary transition period at the initial position was included prior to the next lap. Resultant trunk speed during the walking and transition periods were extracted from an inertial (240 Hz sampling rate) and an optical system (120 Hz sampling rate) to calculate the agreement (Pearson’s correlation coefficient) and relative root mean square errors between both systems. The agreement for the resultant trunk speed between the inertial system and the optical system was strong (0.67 < r ≤ 0.9) for both walking and transition periods. Moreover, relative root mean square error during the transition periods was greater in comparison to the walking periods (>40% across all paths). It was concluded that trunk speed extracted from inertial systems have fair accuracy during walking, but the accuracy was reduced in the transition periods.

Private Filtering for Hidden Markov Models

Consider a hidden Markov model describing a system with two types of states: a monitored state and a private state. The two types of states are dependent and evolve jointly according to a Markov process with a stationary transition probability. It is desired to reveal the monitored states to a receiver but hide the private states. For this purpose, a privacy filter is necessary which suitably perturbs the monitored states before communication with the receiver. Our objective is to design the privacy filter to optimize the tradeoff between the monitoring accuracy and privacy, measured through a time-invariant distortion measure and Shannon's equivocation, respectively. As the optimal privacy filter is difficult to compute using the dynamic programming, we adopt a suboptimal greedy approach through which the privacy filter can be computed efficiently. Here, the greedy approach has the additional advantage of not being restricted to the finite time horizon setups. Simulations show the superiority of the approach compared to a privacy filter which only adds independent noise to the observations.

Supervised kernel approach for automated learning using General Stochastic Networks

Generative Stochastic Networks (GSN) for supervised tasks generalize the denoising autoencoders by fixing the deepest layer to the output variables (e.g. class) and estimate the input–output joint distribution as the stationary transition operator of a Markov chain. Because of multi-layer network architectures with stochastic neurons, GSN performance depends on the selected architecture and network training. Aiming to improve such a performance, we introduce a supervised kernel-based learning within a GSN framework. Firstly, the considered network model induces a temporal model working as a data filtering that extracts refined data representations. Then, we use the conventional exhaustive search strategy to fix the hidden layer size. Lastly, we propose a novel supervised layer-wise pre-training that initializes the fine tuning stage of the GSN with more discriminative projection matrices favoring the optimization of the non-convex cost function. Initial matrices are computed by maximizing the centered-kernel alignment (CKA) metric, measuring the affinity between projected samples and labels. We evaluate the proposal performance in comparison with Random, AutoEncoders, and Principal Component Analysis approaches. As a result, CKA-based pre-training approach captures the complex dependencies between parameters, increases the convergence speed in the learning stage, and unravels the data distribution to favor the class discrimination for five widely image collections used in classification tasks of image object recognition.

Cluster approximations for the TASEP: stationary state and dynamical transition

We develop and test cluster approximations, which generalize simple mean--field by taking into account more and more local correlations, for the Totally Asymmetric Simple Exclusion Process with open boundaries. We consider in detail the pair and triplet approximations, discussing the improvements with respect to mean field in various steady state properties. Moreover, we analyze the recently discovered dynamical transition, describing how the spectrum of the relaxation matrix changes at the transition.

Response Surface Models for OLS and GLS Detrending-based Unit-root Tests in Nonlinear ESTAR Models

In this article, we calculate response surface models for a large range of quantiles of the Kapetanios, Shin, and Snell (2003, Journal of Econometrics 112: 359–379) and Kapetanios and Shin (2008, Economics Letters 100: 377–380) tests for the null hypothesis of a unit root against the alternative—that the series of interest follows a globally stationary exponential smooth transition autoregressive process. The response surface models allow estimation of finite-sample critical values and approximate p-values for different combinations of the number of observations, T, and the lag order in the test regression, p. The latter can be either specified by the user or optimally selected using a data-dependent procedure. We present the new commands kssur and ksur and illustrate their use with an empirical example.

Curve speed model for driver assistance based on driving style classification

Inappropriate speed in negotiating curves is the primary cause of rollovers and sideslips. In this study, the authors proposed an improved curve speed model considering driving styles, as well as vehicle and road factors. On the basis of a vehicle–road interaction model, the driver behaviour factor was introduced to quantify driving styles of curve speed choices. Firstly, the fuzzy synthetic evaluation method was utilised to classify the driving styles of 30 professional drivers into three different types (i.e. cautious, moderate and aggressive). Secondly, the classification results using fuzzy synthetic evaluation were compared to and verified with the K-means clustering method resulting over 60% the similarities. Finally, the proposed curve speed model was built and compared with four existing models. The authors’ model has the following promising advantages: (i) it reflects the speed preferences of three different types of drivers on the premise of driving safety on curves; and (ii) it shows a stationary speed transition when the road adhesion coefficient exceeds 0.8, which indicates that rollover, instead of sideslip, becomes the primary cause for lateral instability crashes on curves. Therefore, this proposed curve speed model could be applied in a curve speed warning system to improve both driving safety and comfort.

Testing for a unit root against ESTAR stationarity

Abstract In this paper we examine the local power of unit root tests against globally stationary exponential smooth transition autoregressive [ESTAR] alternatives under two sources of uncertainty: the degree of nonlinearity in the ESTAR model, and the presence of a linear deterministic trend. First, we show that the KSS test (Kapetanios, G., Y. Shin, and A. Snell. 2003. “Testing for a Unit Root in the Nonlinear STAR Framework.” Journal of Econometrics 112: 359–379) for nonlinear stationarity has local asymptotic power gains over standard Dickey-Fuller [DF] tests for certain degrees of nonlinearity in the ESTAR model, but that for other degrees of nonlinearity, the linear DF test has superior power. Second, we derive limiting distributions of demeaned, and demeaned and detrended KSS and DF tests under a local ESTAR alternative when a local trend is present in the DGP. We show that the power of the demeaned tests outperforms that of the detrended tests when no trend is present in the DGP, but deteriorates as the magnitude of the trend increases. We propose a union of rejections testing procedure that combines all four individual tests and show that this captures most of the power available from the individual tests across different degrees of nonlinearity and trend magnitudes. We also show that incorporating a trend detection procedure into this union testing strategy can result in higher power when a large trend is present in the DGP.

Investigation of the nonlinear behaviour in real exchange rates in developing regions

ABSTRACTThis article examines whether the purchasing power parity (PPP) theory holds or not for the economies in different developing regions located in Africa, Asia and Latin America. In order to investigate this issue, a nonlinear panel unit root test is used to determine if some or all of the real exchange rates in a panel follow a stationary exponential smooth transition autoregressive process. By applying the nonlinear panel unit root test, our results demonstrate an empirical support for the theory of PPP for the economies in developing regions.

Susceptibility of Salmonella enterica Isolates from Tomato Farm Environments to Fatty Acids Naturally Found on Tomato Fruit.

Salmonella enterica subsp. enterica can colonize tomato fruit as it interacts with fruit surface compounds. The exometabolome of tomato fruit contains a mixture of compounds, including fatty acids, which could affect Salmonella fitness. Fatty acids detected in fruit exudates were investigated for Salmonella inhibition. Pelargonic, lauric, myristic, palmitic, margaric, stearic, and oleic acids were suspended in water dissolved in dimethyl sulfoxide (DMSO) or emulsified in water and quillaja saponin to assess how bioavailability impacted Salmonella growth. The minimum inhibitory concentrations of fatty acids were determined using a resazurin assay. Quillaja saponin emulsion and DMSO solution of pelargonic acid were inhibitory to Salmonella at 31.25 mM. Lauric and myristic acid emulsions inhibited growth at 1 M concentrations in quillaja emulsions and 62.5 mM in DMSO. Lauric and myristic acids significantly affected growth of Salmonella Newport, Javiana, and Typhimurium (p ≤ 0.05). Growth curve analysis using the Baranyi model revealed reduced maxima populations for all treatments (p ≤ 0.001) and shorter lag phase durations for Salmonella Newport with lauric acid (p < 0.01) and Salmonella Javiana with lauric (p < 0.001) and myristic (p < 0.001) acids. Salmonella Newport and Javiana exhibited an accelerated growth rate with lauric acid (p < 0.001) as a result of early stationary phase transition (shorter log phase). In myristic acid-amended media, Salmonella Javiana also displayed a faster growth rate (p < 0.001). Pelargonic acid (31.25 mM) treatment of Salmonella cells resulted in a drop in culturable cells to below detection in an hour. Microscopic analysis with Cyto-dye and propidium iodide of bacterial cells treated with pelargonic acid indicated a mixture of live and dead cells, with cell lysis of some cells. A subset of cells exhibited elongation-possibly indicating filament formation, a known antibiotic stress response. The results suggest that fatty acids present in tomato fruit surface exudates may exert a restrictive effect on Salmonella growth on fruit.

Evaluating the dynamics and impact of terrorist attacks on tourism and economic growth for Turkey

ABSTRACTTurkey is one of the most advanced economies in the MENA region and one of the most important tourist destinations in the world, but with a long lasting history of terrorism that has significantly increased during the last years after the Syrian Civil War. This paper uses an updated sample of quarterly data on GDP, inbound tourism and terrorism to explore the relationship between tourism and economic growth adding information on terrorist activity. Different specifications of cointegrating regressions are used to quantify the impact of terrorism on the relationship between tourism demand and economic growth, including the novelty approach of a threshold cointegrating regression, where the stationary transition variable is given by a standard measure of terrorist activity. Results show that, with a delay between three to six months, and even for a relatively small number of estimated terrorist attacks, there is a negative impact of terrorism on real GDP of around 10% through a reduction of the contribution of tourism demand on economic growth.

Computational Investigation of Step Excrescence Sensitivity in a Swept-Wing Boundary Layer

The interaction between an existing stationary crossflow instability and a two-dimensional step excrescence placed into a swept boundary layer on a lifting surface has been investigated computationally. Backward-facing steps were found to not amplify the stationary modes. The existence of an additional local traveling instability was found in the recirculation region following the step. Further time-resolved calculations were recommended to confirm this mode as the cause of breakdown. Forward-facing steps were found to affect the transition location only once a critical step height was exceeded. This height was associated with sudden amplification of stationary crossflow modes and transition moving forward to the step. A physics-based correlation based on linear stability theory and associated with the height of the core of the stationary crossflow vortex was proposed for that critical height, which agreed well with results for the geometry and Reynolds numbers tested experimentally. Critical forward-facing step heights were in the majority of cases higher than those associated with backward-facing steps for the same conditions. Moreover, the physics associated with two-dimensional excrescences in swept-wing flow was fundamentally different from that associated with other types of roughness, as well as all roughnesses in two-dimensional flowfields. Upon further validation, the proposed correlation for forward-facing steps would provide a reasonable manufacturing criteria available to airframe designers that would prove less restrictive and more accurate than existing empirical criteria.

Determinants of transition in artificially discrete Markov chains using microdata

We describe an econometric procedure to model transitions in Markov chains whose state space is finite and classification stems from observed continuous variables. We show how stationary and non-stationary transition probabilities as well as the marginal effects of continuous and dichotomous variables determining transition can be estimated. The model resembles the ordered probit approach used in Epstein et al. (2006) but allows for the differences in the nature of the dependent variable and suggests some very important extensions pertaining to more meaningful representation of parameter estimates and the simultaneous construction of transition matrices.

First-principles simulation of light propagation and exciton dynamics in metal cluster nanostructures

We present a theoretical approach for the simulation of the electric field and exciton propagation in ordered arrays constructed of molecular-sized noble metal clusters bound to organic polymer templates. In order to describe the electronic coupling between individual constituents of the nanostructure we use the ab initio parameterized transition charge method which is more accurate than the usual dipole–dipole coupling. The electronic population dynamics in the nanostructure under an external laser pulse excitation is simulated by numerical integration of the time-dependent Schrodinger equation employing the fully coupled Hamiltonian. The solution of the TDSE gives rise to time-dependent partial point charges for each subunit of the nanostructure, and the spatio-temporal electric field distribution is evaluated by means of classical electrodynamics methods. The time-dependent partial charges are determined based on the stationary partial and transition charges obtained in the framework of the TDDFT. In order to treat large plasmonic nanostructures constructed of many constituents, the approximate self-consistent iterative approach presented in (Lisinetskaya and Mitric in Phys Rev B 89:035433, 2014) is modified to include the transition-charge-based interaction. The developed methods are used to study the optical response and exciton dynamics of $$\hbox{Ag}_{3}^{+}$$ and porphyrin-Ag4 dimers. Subsequently, the spatio-temporal electric field distribution in a ring constructed of ten porphyrin-Ag4 subunits under the action of circularly polarized laser pulse is simulated. The presented methodology provides a theoretical basis for the investigation of coupled light-exciton propagation in nanoarchitectures built from molecular size metal nanoclusters in which quantum confinement effects are important.

Changes of trehalose content and expression of relative genes during the bioethanol fermentation by Saccharomyces cerevisiae.

Traditionally, trehalose is considered as a protectant to improve the ethanol tolerance of Saccharomyces cerevisiae. In this study, to clarify the changes and roles of trehalose during the bioethanol fermentation, trehalose content and expression of related genes at lag, exponential, and stationary phases (i.e., 2, 8, and 16 h of batch fermentation process) were determined. Although yeast cells at exponential and stationary phase had higher trehalose content than cells at lag phase (P < 0.01), there was no significant difference in trehalose content between exponential and stationary phases (P > 0.05). Moreover, expression of the trehalose degradation-related genes NTH1 and NTH2 decreased at exponential phase in comparison with that at lag phase; compared with cells at lag phase, cells at stationary phase had higher expression of TPS1, ATH1, NTH1, and NTH2 but lower expression of TPS2. During the lag-exponential phase transition, downregulation of NTH1 and NTH2 promoted accumulation of trehalose, and to some extent, trehalose might confer ethanol tolerance to S. cerevisiae before stationary phase. During the exponential-stationary phase transition, upregulation of TPS1 contributed to accumulation of trehalose, and Tps1 protein might be indispensable in yeast cells to withstand ethanol stress at the stationary phase. Moreover, trehalose would be degraded to supply carbon source at stationary phase.

An alternative order-parameter for non-equilibrium generalized spin models on honeycomb lattices

An alternative definition for the order-parameter is proposed, for a family of non-equilibrium spin models with up–down symmetry on honeycomb lattices, and which depends on two parameters. In contrast to the usual definition, our proposal takes into account that each site of the lattice can be associated with a local temperature which depends on the local environment of each site. Using the generalised voter motel as a test case, we analyse the phase diagram and the critical exponents in the stationary state and compare the results of the standard order-parameter with the ones following from our new proposal, on the honeycomb lattice. The stationary phase transition is in the Ising universality class. Finite-size corrections are also studied and the Wegner exponent is estimated as .