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The traditional measurement methods of luminescent materials have some problems, such as low accuracy and large amount of calculation. Therefore, a new measurement method is proposed in this article. Taking molecular capture as the core, the molecular force field is established. According to the first law of hohber_Kong’s molecules captures molecular dynamics, it extracts dynamic simulation data information and takes this as the input to establish the Markov data analysis model. The molecular dynamics data were input, and the multi-index method was used to describe the characteristics of metal complex luminescent materials. According to the external shape of the material and the tightness of molecular blocks, the dynamic observation index of the current complex can be obtained to determine the characteristics of luminescent materials. The experimental data show that the method in this article has higher accuracy, lower amount of calculation and good overall error adjustment effect. It can effectively determine the characteristics of complex luminescent materials.

In a general stochastic multistate promoter model of dynamic messenger ribonucleic acid (mRNA)/protein interactions, we identify the stationary joint distribution of the promoter state, mRNA, and protein levels through an explicit "stick-breaking" construction perhaps of interest in itself. This derivation is a constructive advance over previous work where the stationary distribution is solved only in restricted cases. Moreover, the stick-breaking construction allows us to sample directly from the stationary distribution, permitting inference procedures and model selection. In this context, we discuss numerical Bayesian experiments to illustrate the results. [ABSTRACT FROM AUTHOR]

Statistical learning (SL) is an innate mechanism by which the brain automatically encodes the n-th order transition probability (TP) of a sequence and grasps the uncertainty of the TP distribution. Through SL, the brain predicts a subsequent event (en+1) based on the preceding events (en) that have a length of “n”. It is now known that uncertainty modulates prediction in top-down processing by the human predictive brain. However, the manner in which the human brain modulates the order of SL strategies based on the degree of uncertainty remains an open question. The present study examined how uncertainty modulates the neural effects of SL and whether differences in uncertainty alter the order of SL strategies. It used auditory sequences in which the uncertainty of sequential information is manipulated based on the conditional entropy. Three sequences with different TP ratios of 90:10, 80:20, and 67:33 were prepared as low-, intermediate, and high-uncertainty sequences, respectively (conditional entropy: 0.47, 0.72, and 0.92 bit, respectively). Neural responses were recorded when the participants listened to the three sequences. The results showed that stimuli with lower TPs elicited a stronger neural response than those with higher TPs, as demonstrated by a number of previous studies. Furthermore, we found that participants adopted higher-order SL strategies in the high uncertainty sequence. These results may indicate that the human brain has an ability to flexibly alter the order based on the uncertainty. This uncertainty may be an important factor that determines the order of SL strategies. Particularly, considering that a higher-order SL strategy mathematically allows the reduction of uncertainty in information, we assumed that the brain may take higher-order SL strategies when encountering high uncertain information in order to reduce the uncertainty. The present study may shed new light on understanding individual differences in SL performance across different uncertain situations.

Several measures of geometric discords have been recently proposed and mostly characterized in qubits. In this work, the dynamics of four measures of geometric discords is studied for two kinds of initial states in the two-qutrit model under environments. It is shown that four indicators can display freezing in Markovian regime and multiple freezing with multiple sudden changes in non-Markovian regime for suitable initial states and model parameters. Moreover, those measures evolve in a similar manner, but they differ from the decaying or increasing rate, the oscillatory amplitude and the time interval of freezing. However, they do not exhibit any freezing for the general Bell states. The dynamical hierarchy is discussed for those indicators. Those are helpful to understand the rich phenomena of discordant dynamics in open qutrits. [ABSTRACT FROM AUTHOR]

This paper studies the time-dependent first-in-first-out (FIFO) queuing model with a single intermittently available server and variable-sized bulk arrivals and bulk departures. The time between arrivals, servicing time, and server availability time follows an exponential distribution. The difference-differential equations are used for developing system equations, and the Laplace transforms (L.T.), and inverse transforms are used for the solution. Explicit two-state transient recursive probabilities are obtained for an exact number of bulk arrivals and bulk departures, and in the end, few particular cases are derived. [ABSTRACT FROM AUTHOR]

Brain models music as a hierarchy of dynamical systems that encode probability distributions and complexity (i.e., entropy and uncertainty). Through musical experience over lifetime, a human is intrinsically motivated in optimizing the internalized probabilistic model for efficient information processing and the uncertainty resolution, which has been regarded as rewords. Human's behavior, however, appears to be not necessarily directing to efficiency but sometimes act inefficiently in order to explore a maximum rewards of uncertainty resolution. Previous studies suggest that the drive for novelty seeking behavior (high uncertain phenomenon) reflects human's curiosity, and that the curiosity rewards encourage humans to create and learn new regularities. That is to say, although brain generally minimizes uncertainty of music structure, we sometimes derive pleasure from music with uncertain structure due to curiosity for novelty seeking behavior by which we anticipate the resolution of uncertainty. Few studies, however, investigated how curiosity for uncertain and novelty seeking behavior modulates musical creativity. The present study investigated how the probabilistic model and the uncertainty in music fluctuate over a composer's lifetime (all of the 32 piano sonatas by Ludwig van Beethoven). In the late periods of the composer's lifetime, the transitional probabilities (TPs) of sequential patterns that ubiquitously appear in all of his music (familiar phrase) were decreased, whereas the uncertainties of the whole structure were increased. Furthermore, these findings were prominent in higher-, rather than lower-, order models of TP distribution. This may suggest that the higher-order probabilistic model is susceptible to experience and psychological phenomena over the composer's lifetime. The present study first suggested the fluctuation of uncertainty of musical structure over a composer's lifetime. It is suggested that human's curiosity for uncertain and novelty seeking behavior may modulate optimization and creativity in human's brain.

To obtain proper insight into how structure develops during a protein folding reaction, it is necessary to understand the nature and mechanism of the polypeptide chain collapse reaction, which marks the initiation of folding. Here, the time-resolved fluorescence resonance energy transfer technique, in which the decay of the fluorescence light intensity with time is used to determine the time evolution of the distribution of intra-molecular distances, has been utilized to study the folding of the small protein, monellin. It is seen that when folding begins, about one-third of the protein molecules collapse into a molten globule state (I MG), from which they relax by continuous further contraction to transit to the native state. The larger fraction gets trapped into a metastable misfolded state. Exit from this metastable state occurs via collapse to the lower free energy I MG state. This exit is slow, on a time scale of seconds, because of activation energy barriers. The trapped misfolded molecules as well as the I MG molecules contract continuously and slowly as structure develops. A phenomenological model of Markovian evolution of the polymer chain undergoing folding, incorporating these features, has been developed, which fits well the experimentally observed time evolution of distance distributions. The observation that the "wrong turn" to the misfolded state has not been eliminated by evolution belies the common belief that the folding of functional protein sequences is very different from that of a random heteropolymer, and the former have been selected by evolution to fold quickly. Unlabelled Image [ABSTRACT FROM AUTHOR]

In this paper, we present a new continuous time model for nonstationary correlation structures for longitudinal data. This model, which provides a continuous time analogue to the antedependence model and is thus referred to as the continuous antedependence (CAD) model, is intended to provide more refined correlation models for longitudinal data and to better accommodate sparse (or highly unbalanced) data. A key component of this model is the 'nonstationarity function' which describes nonstationarity as a unidimensional function of time and has an interesting time expansion/contraction interpretation. Focusing on a Markovian version of the model, we develop a novel nonlinear regression model providing nonlinear least square estimators of model parameters. Both unstructured (for nonparametric estimation) and structured versions of the model are presented. We apply the proposed approach to data from the Multicenter AIDS Clinical Study (MACS), with a focus on inference for the nonstationarity function. In simulation studies, we show good properties (low finite sample bias, and high convergence rates and efficiency) of the proposed unstructured model estimator, which compare favorably to those of an alternative maximum likelihood estimator, particularly in sparse data situations. [ABSTRACT FROM AUTHOR]

Brain models music as a hierarchy of dynamical systems that encode probability distributions and complexity (i.e., entropy and uncertainty). Through musical experience over lifetime, a human is intrinsically motivated in optimizing the internalized probabilistic model for efficient information processing and the uncertainty resolution, which has been regarded as rewords. Human's behavior, however, appears to be not necessarily directing to efficiency but sometimes act inefficiently in order to explore a maximum rewards of uncertainty resolution. Previous studies suggest that the drive for novelty seeking behavior (high uncertain phenomenon) reflects human's curiosity, and that the curiosity rewards encourage humans to create and learn new regularities. That is to say, although brain generally minimizes uncertainty of music structure, we sometimes derive pleasure from music with uncertain structure due to curiosity for novelty seeking behavior by which we anticipate the resolution of uncertainty. Few studies, however, investigated how curiosity for uncertain and novelty seeking behavior modulates musical creativity. The present study investigated how the probabilistic model and the uncertainty in music fluctuate over a composer's lifetime (all of the 32 piano sonatas by Ludwig van Beethoven). In the late periods of the composer's lifetime, the transitional probabilities (TPs) of sequential patterns that ubiquitously appear in all of his music (familiar phrase) were decreased, whereas the uncertainties of the whole structure were increased. Furthermore, these findings were prominent in higher-, rather than lower-, order models of TP distribution. This may suggest that the higher-order probabilistic model is susceptible to experience and psychological phenomena over the composer's lifetime. The present study first suggested the fluctuation of uncertainty of musical structure over a composer's lifetime. It is suggested that human's curiosity for uncertain and novelty seeking behavior may modulate optimization and creativity in human's brain. [ABSTRACT FROM AUTHOR]

Highlights • The dynamics of the entropic uncertainty is investigated under the crossover of Markovian and non-Markovian regimes. • The trace distance and non-Markovianity are revealed. • The strategy to reduce the measurement's uncertainty is proposed. Abstract We study the dynamical characteristics of the entropy-based uncertainty with regard to a pair of incompatible measurements under a bipartite qubit-system suffering from quantum decoherence induced by hierarchical environments. How non-Markovian and Markovian environments affect the dynamical behaviors of the measurement's uncertainty is revealed. We prove that the measured uncertainty of interest demonstrates a non-monotonic behavior, viz., the amount will increase initially and subsequently oscillate periodically with the growth of time in a non-Markovian regime; On the contrary, the uncertainty will inflate firstly and monotonically decrease in a Markovian regime. Noteworthily, we put forward a simple and feasible strategy to suppress the damping of the system and hence be good for decreasing the magnitude of the uncertainty, by virtue of optimal combination of pre-weak measurements and post-filtering operations. Furthermore, we explore the applications of the uncertainty relation investigated on entanglement witness and channel capacity. [ABSTRACT FROM AUTHOR]

The behavior of quantum coherence is studied under Markovian and non-Markovian dynamics for open quantum systems. For single qubit systems, we show that the coherence depending on the off-diagonal elements of the density matrix is the upper bound of the coherence depending on the relative entropy under both Markovian and non-Markovian processes. For two-qubit systems, in both Markovian and non-Markovian processes, quantum discord and coherence show less sensitivity to the initial state than quantum entanglement. We also find that the quantum discord has similar behaviors with coherence under both Markovian and non-Markovian dynamics. [ABSTRACT FROM AUTHOR]

This manuscript starts with a detailed analysis of the current solution for the queueing system M/Er/1/∞. In the existing solution, Erlang’s service is caused by Poisson’s arrival process of groups, but not individual clients. The service of individual clients is still exponentially distributed, contrary to the declaration in Kendall’s notation. From the related theory of the Hidden Markov Model (HMM), for the advancement of queueing theory, the idea of “hidden Markov states” (HMS) was taken. In this paper, the basic principles of application of HMS have first been established. The abstract HMS states have a catalytic role in the standard procedure of solving the non-Markovian queueing systems. The proposed solution based on HMS exceeds the problem of accessing identical client groups in the current solution of the M/Er/r queueing system. A detailed procedure for the new solution of the queueing system M/Er/1/∞ is implemented. Additionally, a new solution to the queueing system M/N/1/∞ with a normal service time N(μ, σ) based on HMS is also implemented.

We scrutinize the movie views from the Netflix website and find that there exist four particular temporal patterns. In particular some movies have multiple periods of view creation and destruction. Based on these observations we suggest that there exist stationary states in the human complex behavior. A model based on Markovian process is proposed to describe the dynamics of the movie watching for individual films. The model can reproduce of the peak structure of the real watching curves for a class of films which suggests that concepts of state and transitions in physics can be applied to understand the human complex behavior. [ABSTRACT FROM AUTHOR]

Shearer height adjusting is a key technology for shearer automation. Considering constraints of present shearer height adjusting,this paper built grey-markovian memory cutting algorithm after analyzing traditional shearer memory cutting algorithm. We firstlyconstructed prediction mechanism using grey model, then determined state transition probability matrix using markovian, so as to revisefeedback of grey model and achieve automatic height adjusting. Finally we simulated this model to get reliability. By comparing greymarkovianmemory cutting algorithm with traditional memory cutting algorithm, it is concluded that the grey-markovian model hashigher efficiency and accuracy

Statistical learning (SL) is an innate mechanism by which the brain automatically encodes the n -th order transition probability (TP) of a sequence and grasps the uncertainty of the TP distribution. Through SL, the brain predicts a subsequent event ( e n+1 ) based on the preceding events ( e n ) that have a length of " n" . It is now known that uncertainty modulates prediction in top-down processing by the human predictive brain. However, the manner in which the human brain modulates the order of SL strategies based on the degree of uncertainty remains an open question. The present study examined how uncertainty modulates the neural effects of SL and whether differences in uncertainty alter the order of SL strategies. It used auditory sequences in which the uncertainty of sequential information is manipulated based on the conditional entropy. Three sequences with different TP ratios of 90:10, 80:20, and 67:33 were prepared as low-, intermediate, and high-uncertainty sequences, respectively (conditional entropy: 0.47, 0.72, and 0.92 bit, respectively). Neural responses were recorded when the participants listened to the three sequences. The results showed that stimuli with lower TPs elicited a stronger neural response than those with higher TPs, as demonstrated by a number of previous studies. Furthermore, we found that participants adopted higher-order SL strategies in the high uncertainty sequence. These results may indicate that the human brain has an ability to flexibly alter the order based on the uncertainty. This uncertainty may be an important factor that determines the order of SL strategies. Particularly, considering that a higher-order SL strategy mathematically allows the reduction of uncertainty in information, we assumed that the brain may take higher-order SL strategies when encountering high uncertain information in order to reduce the uncertainty. The present study may shed new light on understanding individual differences in SL performance across different uncertain situations., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

Geometric measures of quantum and classical correlations are being proposed and characterized in qubit systems. In this work, the dynamics of quantum and classical correlations via trace and Hellinger distances is comparatively investigated for various initial states in a two-qutrit model under decoherence. It is shown that quantum correlations exhibit freezing in Markovian regime and multiple freezing with multiple sudden changes in non-Markovian regime, which is dependent on initial states and model parameters. Moreover, both measures evolve similar behaviors, but they are difference in the decaying or increasing rate, the revival amplitude, and the time interval of freezing. Remarkably, the time interval of freezing of trace quantum correlations is longer than that of Hellinger ones. However, the freezing of quantum correlations does not occur for the general Bell state, whereas classical ones are forever freezing. The dynamical hierarchy of both measures of correlations is discussed. Those are helpful to understand the rich phenomena of correlation dynamics in open qutrits. • Trace and Hellinger-distance correlations are studied for various initial states in two open qutrits. • Quantum correlations can exhibit freezing in Markovian regime and multiple freezing in non-Markovian regime. • The time interval of freezing for trace quantum correlations is longer than that for Hellinger ones. • Classical correlations are forever frozen for the general Bell state while quantum correlations do not. • The dynamical hierarchy of both measures is discussed. [ABSTRACT FROM AUTHOR]

The dynamics of atomic condensate loaded in a double-well symmetric traps coupled to distinct dual reservoirs is studied. The effect of damping on the population evolution through the exposure of traps to their respective reservoir has been analysed at various temperatures. Damping in our system is induced by the fluctuation of the reservoir in agreement with the fluctuation–dissipation theorem. We found damping due to strong reservoir correlation destroy the quantum tunnelling state and macroscopic quantum self-trap state of the closed two-mode Bose–Einstein condensate states at all finite temperatures. Alternatively switching the trap’s out-coupling damping rates shows significant change in the population dynamics particularly in the strongly interacting case. Dissipation coupled with strong on-site interaction in the traps exhibits even more interesting dynamics as the equilibrium temperature in system is increased. [ABSTRACT FROM AUTHOR]

In this work the conservative system of N identical two-level atoms coupled with a quantized electromagnetic field, prepared via a single photon Fock state, is investigated. The non-zero initial detuning of the Fock state is under consideration. The present paper reveals and investigates the certain fundamental problems concerning the application of the dipole approximation for the “interaction” of the quantized electromagnetic field with the many-body system. Here, we do not restrict ourselves to any boundary conditions and do not apply the “Weisskopf–Wigner” and “Markovian” approximations. We developed technique to estimate the most “problematic” integral expressions appearing in the quantum optic theory of a photon scattering by a polyatomic system. The Laplace transformation was applied in order to solve the corresponding N -particle evolution equations. It was shown, the dipole approximation, that is determined by a gauge non-invariant Hamiltonian, can result in certain irreversible processes during the system evolution with time. Namely, possible collective (coherent) modes for the probability amplitudes decay with time. At the same time, the mode corresponding to the field detuning frequency does not vanish. However, the ability to decay vanishes for the collective (coherent) modes, when the used inverse Laplace transformation undergoes a commutation in the specific limits of the problem. The investigated here structure of the solutions for the evolution equations therefore raises the fundamental problem about the correct representation of a damping phenomena in the commonly accepted approximations. The corresponding N -particle state amplitudes are calculated for the several space configurations of the closed conservative system. The elaborated theory allows to describe analytically the evolution of the system components with time for any space configurations, including the cases with relative distances between atoms shorter or larger than the emission wavelength. [ABSTRACT FROM AUTHOR]

Aim While early warning scores (EWS) have the potential to identify physiological deterioration in an acute care setting, the implementation of EWS in clinical practice has yet to be fully realized. The primary aim of this study is to identify optimal patient-centered rapid response team (RRT) activation rules using electronic medical records (EMR)-derived Markovian models. Methods The setting for the observational cohort study included 38,356 adult general floor patients hospitalized in 2011. The national early warning score (NEWS) was used to measure the patient health condition. Chi-square and Kruskal Wallis tests were used to identify statistically significant subpopulations as a function of the admission type (medical or surgical), frailty as measured by the Braden skin score, and history of prior clinical deterioration (RRT, cardiopulmonary arrest, or unscheduled ICU transfer). Results Statistical tests identified 12 statistically significant subpopulations which differed clinically, as measured by length of stay and time to re-admission ( P < .001). The Chi-square test of independence results showed a dependency structure between subsequent states in the embedded Markov chains ( P < .001). The SMDP models identified two sets of subpopulation-specific RRT activation rules for each statistically unique subpopulation. Clinical deterioration experience in prior hospitalizations did not change the RRT activation rules. The thresholds differed as a function of admission type and frailty. Conclusions EWS were used to identify personalized thresholds for RRT activation for statistically significant Markovian patient subpopulations as a function of frailty and admission type. The full potential of EWS for personalizing acute care delivery is yet to be realized. [ABSTRACT FROM AUTHOR]

We study the dynamics of an atomic condensate loaded in two symmetric traps where one of the trap is coupled to a reservoir. Analytical solution of the population dynamics is given for the non-interacting two-mode Bose–Einstein condensate system in the Markovian limit. In our model dissipation is induced by the reservoir fluctuation in agreement with the Fluctuation–Dissipation theorem. For the interacting two-mode case however, we resort to numerical calculation to study the effect of damping on the population evolution. We have shown that dissipation acting concurrently with repulsion interaction drives the BEC self trapping state back to the quantum tunnelling state for the zero-temperature case. For the finite temperature case however much surprising dynamics were observed as the tunnelling between the traps is suppressed when the temperature of the system is increased. [ABSTRACT FROM AUTHOR]

As a memory channel, power-line communication (PLC) channel can be modeled by the finite states Morkov model. The memory order in an finite states Morkov model is a key parameter, which is not solved in PLC systems yet. In the paper, the mutual information is used as a measure of the dependence between the different symbols, treated as the received scalar network analyzer, or amplitude of the current channel symbol or that of previous symbols. The joint distribution probabilities of the envelopes in PLC systems are computed based on the multi-path channel model, which is commonly used in PLC. With the first-order Markovian assumption, we confirm that given the information of the symbol immediately preceding the current one, any other previous symbol is independent of the current one in PLC systems. The field test is also performed to model the received OFDM signals with the help of autoregressive model. The results show that the first-order autoregressive model is enough to model the fading channel in PLC systems, which means the amount of uncertainty remaining in the current symbol should be negligible, given the information corresponding to the immediately preceding one. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

We analytically obtain the precision bounds of frequency measurements in correlated Markovian and non-Markovian environments using a variational approach. The metrological equivalence of product states and maximally entangled states persisting in maximally correlated Markovian and non-Markovian environments is verified using a standard Ramsey spectroscopy setup. Furthermore, we find that optimal measurements can be used to achieve a much higher resolution than standard Ramsey spectroscopy in correlated environments. [ABSTRACT FROM AUTHOR]

Abstract: We propose a clustering method maximizing a new measure called “group dependence.” Group dependence quantifies how precise a certain division of a graph is in terms of dependence distance. Built upon statistical dependence measure between points driven by Markovian transitions, group dependence incorporates the geometric structure of input data. Besides capturing degrees of positive dependence and coherence for a group division, group dependence inherently supplies the proposed clustering method with a definite decision on the depth of division. We provide an optimality aspect of the method as theoretical justification in consideration of posterior transition probabilities of input data. Illustrating its procedure using data from a known structure, we demonstrate its performance in the clustering task of real-world data sets, Amazon, DBLP, and YouTube, in comparison with selected clustering algorithms. We show that the proposed method outperforms the selected methods in reasonable settings: in particular, the proposed method surpasses modularity clustering in terms of normalized mutual information. We also show that the proposed method reveals additional insights on community structure detection according to its connectivity scale parameter. [Copyright &y& Elsevier]

We study a multiclass Markovian queueing network with switchover across a set of many-server stations. New arrivals to each station follow a nonstationary Poisson process. Each job waiting in queue may, after some exponentially distributed patience time, switch over to another station or leave the network following a probabilistic and state-dependent mechanism. We analyze the performance of such networks under the many-server heavy-traffic limiting regimes, including the critically loaded quality-and-efficiency-driven (QED) regime, and the overloaded efficiency-driven (ED) regime. We also study the limits corresponding to mixing the underloaded quality-driven (QD) regime with the QED and ED regimes. We establish fluid and diffusion limits of the queue-length processes in all regimes. The fluid limits are characterized by ordinary differential equations. The diffusion limits are characterized by stochastic differential equations, with a piecewise-linear drift term and a constant (QED) or time-varying (ED) covariance matrix. We investigate the load balancing effect of switchover in the mixed regimes, demonstrating the migration of workload from overloaded stations to underloaded stations and quantifying the load balancing impact of switchover probabilities. [ABSTRACT FROM AUTHOR]

In this paper, we report on a detailed study of how decoherence assists quantum trans-port in a dimer system. By carrying out analytical and numerical computations, we find that the decoherence induced by a Markovian (memoryless) environment only assists the maximum probability of quantum transport in the dimer system to 50% not close to the unity, and the decoherence induced by a non-Markovian environment with perfect mem-ory (infinite memory time) can improve the maximum probability of quantum transport to unity. The combination of non-Markovian decoherence and Markovian decoherence assist the quantum transport more effectively. Comparing the classical environment with the quantum environment, we obtain that the classical non-Markovian environment can-not assist the maximum probability of quantum transport close to unity. In addition, enlarging the quantum environment can improve the maximum probability of quantum transport. [ABSTRACT FROM AUTHOR]

Livelihood systems that depend on mobile resources must constantly adapt to change. For people living in permanent settlements, environmental changes that affect the distribution of a migratory species may reduce the availability of a primary food source, with the potential to destabilize the regional social-ecological system. Food security for Arctic indigenous peoples harvesting barren ground caribou (Rangifer tarandus granti) depends on movement patterns of migratory herds. Quantitative assessments of physical, ecological, and social effects on caribou distribution have proven difficult because of the significant interannual variability in seasonal caribou movement patterns. We developed and evaluated a modeling approach for simulating the distribution of a migratory herd throughout its annual cycle over a multiyear period. Beginning with spatial and temporal scales developed in previous studies of the Porcupine Caribou Herd of Canada and Alaska, we used satellite collar locations to compute and analyze season-by-season probabilities of movement of animals between habitat zones under two alternative weather conditions for each season. We then built a set of transition matrices from these movement probabilities, and simulated the sequence of movements across the landscape as a Markov process driven by externally imposed seasonal weather states. Statistical tests showed that the predicted distributions of caribou were consistent with observed distributions, and significantly correlated with subsistence harvest levels for three user communities. Our approach could be applied to other caribou herds and could be adapted for simulating the distribution of other ungulates and species with similarly large interannual variability in the use of their range. [ABSTRACT FROM AUTHOR]

The predictions of the Quantum Theory have been verified so far with astonishingly high accuracy. Despite of its impressive successes, the theory still presents mysterious features such as the border line between the classical and quantum world, or the deep nature of quantum nonlocality. These open questions motivated in the past several proposals of alternative and/or generalized approaches. We shall discuss in the present paper alternative theories that can be infered from a reconsideration of the status of time in quantum mechanics. Roughly speaking, quantum mechanics is usually formulated as a memory free (Markovian) theory at a fundamental level, but alternative, nonMarkovian, formulations are possible, and some of them can be tested in the laboratory. In our paper we shall give a survey of these alternative proposals, describe related experiments that were realized in the past and also formulate new experimental proposals. [ABSTRACT FROM AUTHOR]

We consider the population and decay of a qubit under the electromagnetic environment. Employing the timeconvolutionless master equation, we investigate the Markovian and non-Markovian behaviour of the corresponding perturbation expansion. The Jaynes-Cummings model on resonance is investigated. Some figures clearly show the different evolution behaviours. The reasons are interpreted in the paper. [ABSTRACT FROM AUTHOR]

This article considers a first-come first-served single-server system with opening and closing times. Service durations are exponentially distributed, and the total number of arrivals is a Poisson random variable. Naturally each customer wishes to minimize his/her waiting time. The process of choosing an arrival time is presented as a (non-cooperative) multi-player game. The overall goal of this work is to find a Nash equilibrium game strategy. It is assumed in the literature that arrivals before the opening time of the system are allowed. In this work the case where early arrivals are forbidden is studied. It turns out that unless the system is very heavily loaded, the equilibrium solution with such a restriction does not reduce the expected waiting time in a significant way. The equilibrium solution is compared with the solution which maximizes social welfare. Finally, it is show that social welfare can be increased in equilibrium by restricting arrivals to certain points of time. [ABSTRACT FROM AUTHOR]

Monte Carlo (MC) studies of flow in heterogeneous porous formations, in which the log-conductivity field is multi-Gaussian, have shown that as the log-conductivity variance σ Y2 increases beyond about 0.5, the one-point velocity probability density functions (PDFs) deviate significantly from Gaussian behavior. The velocity statistics become more complex due to the formation of preferential flow paths, or channels, as σ Y2 increases. Methods that employ low-order approximations (e.g., truncated perturbation expansions) are limited to small σ Y2 and are unable to represent the complex velocity statistics associated with σ Y2 > 1. Here a stochastic transport model for highly heterogeneous domains (i.e., σ Y2 > 1) is proposed. In the model, the Lagrangian velocity components of tracer particles are represented using continuous Markovian stochastic processes in time. The Markovian velocity process (MVP) model is described using a set of first-order ordinary and stochastic differential equations, which are easy to solve using a particle-based method. Once the MVP model is calibrated based on velocity statistics from MC simulations of the flow (hydraulic head and velocity), the MVP-based model can be used to describe the evolution of the tracer concentration field accurately and efficiently. Specifically, the MVP model is validated using MC simulations of longitudinal and transverse tracer spreading due to a point-like injection in the domain. We demonstrate that for σ Y2 > 1, the ensemble-averaged tracer cloud remains markedly non-Gaussian for relatively large travel distances from the point source. The MVP transport model captures this behavior and reproduces the velocity statistics quite accurately. [ABSTRACT FROM AUTHOR]

As a continuous-time model, forward-backward stochastic differential equations (in short FBSDEs) have been successfully applied in mathematical finance, e.g., European option pricing for either a small or a large investor in a Markovian market. However, the correct FBSDEs model for a specific topic can neither be provided automatically by financial market nor derived from theory of mathematical finance. In this article, a nonparametric FBSDEs model is adopted for its flexibility and robustness, and the estimators of the functional coefficients of the FBSDEs model are obtained. The asymptotic properties of the estimators are also discussed. A simulation is performed to test the feasibility of our method. [ABSTRACT FROM AUTHOR]

This article mainly presents applications of previously derived formulas by the author to experimental systems whereby the Markovian and non-Markovian multimode Brownian oscillator (MBO) model and their consequent dynamics are explored. These applications include computing the zero-phonon line (ZPL) widths of aluminum phthalocyanine tetrasulphonate (APT) in glassy films of water and ethanol, which are compared to those of the Ohmic MBO model-calculated ZPL widths at different temperatures. The analytical forms of the ZPL width and Franck–Condon factors (FCF) derived from the high-temperature limit underdamped MBO model absorption line shape (Toutounji, Chem Phys, 2003, 293, 311) are recovered from the finite-temperature MBO model, which includes Matsubara terms (Toutounji and Small, J Chem Phys, 2002, 117, 3848). As the applicability of the Ohmic MBO model at low temperatures is questionable, the corresponding low-temperature (T) Markovian dynamics is discussed. A formula for the Ohmic MBO model ZPL FCF at T = 0 is presented. It is established that this formula reduces to e-S, S is Huang–Rhys factor, at T = 0, which further ratifies our previous conclusion (Toutounji and Small, J Chem Phys, 2002, 117, 3848) that the bath modes are completely thwarted from contributing to the ZPL profile at this T. Hayes–Small theory of linear absorption and hole-burning line shapes is discussed and compared to that of the MBO model and other line shapes. Overdamped Ohmic MBO model is briefly discussed. Illustrative calculations are presented. © 2009 Wiley Periodicals, Inc. Int J Quantum Chem, 2009 [ABSTRACT FROM AUTHOR]

This paper deals with the analysis of an infinite capacity Markovian queueing model with number of heterogeneous channels depending on system size. The model consists of one regular channel and a maximum of c additional channels. By defining generating function and using the properties of Bessel functions, the transient probabilities and hence the steady state results are derived. [ABSTRACT FROM AUTHOR]

A bioeconomic model of cham scallop aquaculture in Korea was used to determine an optimal farm grow-out strategy. The model used estimated cost functions for two culture methods and growth functions based on three environmental characteristics for each region (Kojin and Chumunjin). Stochastic growth was incorporated using probabilities of alternative environmental states (based on expected levels of water temperature, salinity, and dissolved oxygen) by region, culture method, and month. The model revealed a 26% potential increase in net present value from altering management of a single rotation. Optimal multiple rotations required up to four different rotations depending on the region and culture style. Farmers could increase economic returns by delaying harvest and subsequent plantings in order to market larger, more valuable scallops. Resource managers could increase resource rents by adjusting the licensing system to account for regional-style differences and by targeting the planned aquaculture investments. [ABSTRACT FROM AUTHOR]

Abstract: A Markovian dichotomic system driven by a deterministic time-periodic force is analyzed in terms of the statistical properties of the switching events between the states. The consideration of the counting process of the switching events leads us to define a discrete phase. We obtain expressions for the instantaneous output frequency and phase diffusion associated to the dichotomic process, as well as for their cycle averages. These expressions are completely determined by the rates of escape from both states. They are a convenient starting point for the study of the stochastic frequency and phase synchronization in a wide range of situations (both classical and quantum) in which two metastable states are involved. [Copyright &y& Elsevier]

Markovian bridge deterioration models have been in use in the United States since the early 1990s, using the AASHTO Guide to Commonly-Recognized Structural Elements. California has one of the oldest databases of inspection history using this standard. A preliminary analysis was performed on the California data set, to quantify the deterioration transition probabilities actually observed, and to determine whether it is yet possible to validate the key assumptions of Markovian bridge deterioration models. Several important conclusions were reached, which should provide guidance for future research and implementation. [ABSTRACT FROM AUTHOR]

This paper is the experimental follow-up to Langville and Stewart (2002), where the theoretical background for the nearest Kronecker product (NKP) preconditioner was developed. Here we test the NKP preconditioner on both Markov chains (MCs) and stochastic automata networks (SANs). We conclude that the NKP preconditioner is not appropriate for general MCs, but is very effective for a MC stored as a SAN. [ABSTRACT FROM AUTHOR]

The probabilistic distribution of identical successive units amounts to filling an urn with balls of different colors (one color per agent). The fixed chances methods allocate each unit independently of the current distribution of shares. The Polya–Eggenberger methods place in an urn a fixed number of balls and draw from the urn with replacement of two balls of the color drawn. These methods stand out axiomatically under: a version of Consistency; Increasing Shares (the probability of receiving the next ball is non-decreasing in one''s share), Independence under Transfers (transferring balls across agents is not profitable), and Order Independence (a sequence of successive allocations is as likely as any permuted sequence). On the other hand, Decreasing Shares (the probability of receiving the next ball is non-increasing in one''s share) leads to methods equalizing individual shares either along a fixed standard of comparison, or in proportion to the deviation from a fixed urn. [Copyright &y& Elsevier]

Time-homogeneous diffusion models have been widely used for describing the stochastic dynamics of the underlying economic variables. Recently, Stanton proposed drift and diffusion estimators based on a higher-order approximation scheme and kernel regression method. He claimed that "higher order approximations must outperform lower order approximations" and concluded nonlinearity in the instantaneous return function of short-term interest rates. To examine the impact of higher-order approximations, we develop general and explicit formulas for the asymptotic behavior of both drift and diffusion estimators. We show that these estimators will reduce the numerical approximation errors in asymptotic biases, but their asymptotic variances escalate nearly exponentially with the order of approximation. Simulation studies also confirm our asymptotic results. This variance inflation problem arises not only from nonparametric fitting, but also from parametric fitting. Stanton's work also postulates the interesting question of whether the short-term rate drift is nonlinear. Based on empirical simulation studies, Chapman and Pearson suggested that the nonlinearity might be spurious, due partially to the boundary effect of kernel regression. This prompts us to use the local linear fit based on the first-order approximation, proposed by Fan and Yao, to ameliorate the boundary effect and to construct formal tests of parametric financial models against the nonparametric alternatives. Our simulation results show that the local linear method indeed outperforms the kernel approach. Furthermore, our nonparametric "generalized likelihood ratio tests" are indeed versatile and powerful in detecting nonparametric alternatives. Using this formal testing procedure, we show that the evidence against the linear drift of the short-term interest rates is weak, whereas evidence against a family of popular models for the volatility function is very strong. Application to Standard & Poor 500... [ABSTRACT FROM AUTHOR]

We present a multi-stage conditional quantile predictor for time series of Markovian structure. It is proved that at any quantile level, p ∈ (0, 1), the asymptotic mean squared error (MSE) of the new predictor is smaller than the single-stage conditional quantile predictor. A simulation study confirms this result in a small sample situation. Because the improvement by the proposed predictor increases for quantiles at the tails of the conditional distribution function, the multi-stage predictor can be used to compute better predictive intervals with smaller variability. Applying this predictor to the changes in the U.S. short-term interest rate, rather smooth out-of-sample predictive intervals are obtained. [ABSTRACT FROM AUTHOR]