Analytic prediction of unconfined boundary layer flashback limits in premixed hydrogen–air flames

Flame flashback is a major challenge in premixed combustion. Hence, the prediction of the minimum flow velocity to prevent boundary layer flashback is of high technical interest. This paper presents an analytic approach to predicting boundary layer flashback limits for channel and tube burners. The model reflects the experimentally observed flashback mechanism and consists of a local and global analysis. Based on the local analysis, the flow velocity at flashback initiation is obtained depending on flame angle and local turbulent burning velocity. The local turbulent burning velocity is calculated in accordance with a predictive model for boundary layer flashback limits of duct-confined flames presented by the authors in an earlier publication. This ensures consistency of both models. The flame angle of the stable flame near flashback conditions can be obtained by various methods. In this study, an approach based on global mass conservation is applied and is validated using Mie-scattering images from a channel burner test rig at ambient conditions. The predicted flashback limits are compared to experimental results and to literature data from preheated tube burner experiments. Finally, a method for including the effect of burner exit temperature is demonstrated and used to explain the discrepancies in flashback limits obtained from different burner configurations reported in the literature.     

The prediction of airborne sound transmission between two rooms using first-order flanking paths

Airborne sound transmission between adjacent rooms can be predicted using the Standard EN 12354-1 (ISO 15712-1), which is equivalent to a first-order approximation of statistical energy analysis (SEA). This paper analyses airborne sound transmission between adjacent rooms in a masonry building, by comparing results obtained from EN 12354-1 to SEA predictions and measurements. It is shown that the restriction of the Standard to first-order flanking paths can lead to large errors in predictions when compared to measurements and SEA results taking into account all transmission paths. This is observed both for individual flanking paths and overall transmission between rooms, for which the Standard provides results similar to those obtained by the first-order approximation of SEA. The paper also looks at possible reasons why previous studies using the approach in EN 12354 have generally shown good agreement with measurements.     

Joint statistical moments of mode amplitudes at different frequencies in a random acoustic waveguide

This paper considers an approximate ray-based analytical approach for evaluating the joint statistical moments of mode amplitudes in an acoustic waveguide with large-scale sound speed fluctuations. Explicit analytic expressions for the statistical moments are derived from an approximate analytical solution of the mode-coupling equations. The analytic solution allows one to deduce a scaling law establishing the connection between amplitudes of modes with the same ratio of the mode number to frequency. The applicability of the ray-based approach for evaluating the joint statistical moments of modes amplitudes including the moments at different frequencies is demonstrated using the Monte Carlo simulation of sound fields in a model of the underwater acoustic waveguide in a deep ocean. Numerical simulation also shows different manifestations of the scaling law. In particular, it is shown how this law manifests itself in the acoustic energy distribution between normal modes, in the jump-like range variations of statistical moments, and in the shape of the correlation function between amplitudes of different modes at different frequencies.     

The log-periodic-AR(1)-GARCH(1,1) model for financial crashes

This paper intends to meet recent claims for the attainment of more rigorous statistical methodology within the econophysics literature. To this end, we consider an econometric approach to investigate the outcomes of the log-periodic model of price movements, which has been largely used to forecast financial crashes. In order to accomplish reliable statistical inference for unknown parameters, we incorporate an autoregressive dynamic and a conditional heteroskedasticity structure in the error term of the original model, yielding the log-periodic-AR(1)-GARCH(1,1) model. Both the original and the extended models are fitted to financial indices of U. S. market, namely S&P500 and NASDAQ. Our analysis reveal two main points: (i) the log-periodic-AR(1)-GARCH(1,1) model has residuals with better statistical properties and (ii) the estimation of the parameter concerning the time of the financial crash has been improved.     

Markovian description of unbiased polymer translocation

We perform, with the help of cloud computing resources, extensive Langevin simulations which provide compelling evidence in favor of a general Markovian framework for unbiased three-dimensional polymer translocation. Our statistical analysis consists of careful evaluations of (i) two-point correlation functions of the translocation coordinate and (ii) the empirical probabilities of complete polymer translocation (taken as a function of the initial number of monomers on a given side of the membrane). We find good agreement with predictions derived from the Markov chain approach recently addressed in the literature by the present authors.     

Multiscaling and localized instabilities in fracture, fragmentation, and growth processes

It is shown that localized instabilities can be an origin of log-normal and power-law statistical distributions in fracture, fragmentation and island growth processes. Results of laboratory experiments and numerical simulations performed by different authors are used to demonstrate the applicability of this approach. Received 2 September 1999     

Sensitivity of footbridge vibrations to stochastic walking parameters

Some footbridges are so slender that pedestrian traffic can cause excessive vibrations and serviceability problems. Design guidelines outline procedures for vibration serviceability checks, but it is noticeable that they rely on the assumption that the action is deterministic, although in fact it is stochastic as different pedestrians generate different dynamic forces. For serviceability checks of footbridge designs it would seem reasonable to consider modelling the stochastic nature of the main parameters describing the excitation, such as for instance the load amplitude and the step frequency of the pedestrian. A stochastic modelling approach is adopted for this paper and it facilitates quantifying the probability of exceeding various vibration levels, which is useful in a discussion of serviceability of a footbridge design. However, estimates of statistical distributions of footbridge vibration levels to walking loads might be influenced by the models assumed for the parameters of the load model (the walking parameters). The paper explores how sensitive estimates of the statistical distribution of vertical footbridge response are to various stochastic assumptions for the walking parameters. The basis for the study is a literature review identifying different suggestions as to how the stochastic nature of these parameters may be modelled, and a parameter study examines how the different models influence estimates of the statistical distribution of footbridge vibrations. By neglecting scatter in some of the walking parameters, the significance of modelling the various walking parameters stochastically rather than deterministically is also investigated providing insight into which modelling efforts need to be made for arriving at reliable estimates of statistical distributions of footbridge vibrations. The studies for the paper are based on numerical simulations of footbridge responses and on the use of Monte Carlo simulations for modelling the stochastic nature of actions of a single pedestrian traversing various pin-supported single-span footbridges with frequencies in vertical bending in the range of 1.6-2.4 Hz.     

Image Statistics Preserving Encrypt-then-Compress Scheme Dedicated for JPEG Compression Standard

In this paper, the authors analyze in more details an image encryption scheme, proposed by the authors in their earlier work, which preserves input image statistics and can be used in connection with the JPEG compression standard. The image encryption process takes advantage of fast linear transforms parametrized with private keys and is carried out prior to the compression stage in a way that does not alter those statistical characteristics of the input image that are crucial from the point of view of the subsequent compression. This feature makes the encryption process transparent to the compression stage and enables the JPEG algorithm to maintain its full compression capabilities even though it operates on the encrypted image data. The main advantage of the considered approach is the fact that the JPEG algorithm can be used without any modifications as a part of the encrypt-then-compress image processing framework. The paper includes a detailed mathematical model of the examined scheme allowing for theoretical analysis of the impact of the image encryption step on the effectiveness of the compression process. The combinatorial and statistical analysis of the encryption process is also included and it allows to evaluate its cryptographic strength. In addition, the paper considers several practical use-case scenarios with different characteristics of the compression and encryption stages. The final part of the paper contains the additional results of the experimental studies regarding general effectiveness of the presented scheme. The results show that for a wide range of compression ratios the considered scheme performs comparably to the JPEG algorithm alone, that is, without the encryption stage, in terms of the quality measures of reconstructed images. Moreover, the results of statistical analysis as well as those obtained with generally approved quality measures of image cryptographic systems, prove high strength and efficiency of the scheme’s encryption stage.     

LETTER: Entropy Using Path Integrals for Quantum Black Hole Models

Canonical quantum gravity has been used in the search for eigenvalue equations that could describe black holes. In this paper we choose one of the simplest of these quantum equations to show how the usual Feynman's path integral approach can be applied to get the corresponding statistical properties. We get a logarithmic correction to the Bekenstein–Hawking entropy as already obtained by other authors by other means.     

‘Return to Equilibrium’ for Weakly Coupled Quantum Systems: A Simple Polymer Expansion

Recently, several authors studied small quantum systems weakly coupled to free boson or fermion fields at positive temperature. All the rigorous approaches we are aware of employ complex deformations of Liouvillians or Mourre theory (the infinitesimal version of the former). We present an approach based on polymer expansions of statistical mechanics. Despite the fact that our approach is elementary, our results are slightly sharper than those contained in the literature up to now. We show that, whenever the small quantum system is known to admit a Markov approximation (Pauli master equation aka Lindblad equation) in the weak coupling limit, and the Markov approximation is exponentially mixing, then the weakly coupled system approaches a unique invariant state that is perturbatively close to its Markov approximation.     

Fractional calculus approach to the statistical characterization of random variables and vectors

Fractional moments have been investigated by many authors to represent the density of univariate and bivariate random variables in different contexts. Fractional moments are indeed important when the density of the random variable has inverse power-law tails and, consequently, it lacks integer order moments. In this paper, starting from the Mellin transform of the characteristic function and by fractional calculus method we present a new perspective on the statistics of random variables. Introducing the class of complex moments, that include both integer and fractional moments, we show that every random variable can be represented within this approach, even if its integer moments diverge. Applications to the statistical characterization of raw data and in the representation of both random variables and vectors are provided, showing that the good numerical convergence makes the proposed approach a good and reliable tool also for practical data analysis.     

Study of graphene Maxwell nanofluid flow past a linearly stretched sheet: A numerical and statistical approach

This study aims to unfold the significance of numerous physical parameters such as magnetic field, heat absorption, thermal radiation, viscous and Joule dissipations, etc. on the flow of graphene Maxwell nanofluid over a linearly stretched sheet with considerations of momentum and thermal slip conditions. The prevailing mathematical equations are reformed into extremely nonlinear coupled ordinary differential equations (ODE) utilizing similarity variables and then the equations are solved numerically by the scheme of Runge-Kutta Fehlberg method along with the shooting technique. The variations in graphene Maxwell nanofluid velocity and temperature owing to different physical parameters are shown via numerous graphs whereas numerical values of skin friction coefficients and Nusselt numbers are illustrated and reported in different tables. In addition, statistical approach is followed for the multiple regression estimation analysis on the numerical findings of wall velocity gradient and local Nusselt number and are reported in tabular form to demonstrate the relationship among the heat transfer rate and physical parameters. Our results reveal that the graphene Maxwell nanofluid velocity gets reduced owing to enhancement in magnetic field, angle of inclination of magnetic field, porosity and unsteadiness parameters whereas behavior of nanofluid velocity is reversed due to Maxwell parameter. Further, it is noticed that the heat transfer rate of nanofluid is augmented owing to heat absorption, radiation and thermal slip parameters while it is reduced due to increase in viscous dissipation and unsteadiness parameters. The numerical results of the paper are validated by making comparisons with the earlier published paper under the restricted conditions and we found an excellent agreement with those results. A careful review of research papers reported in literature reveals that none of the authors has attempted this problem earlier although the thoughts and methodology explained in this paper can be anticipated to lead to enormously prolific connections across disciplines.     

On the efficiency of sovereign bond markets

The existence of memory in financial time series has been extensively studied for several stock markets around the world by means of different approaches. However, fixed income markets, i.e. those where corporate and sovereign bonds are traded, have been much less studied. We believe that, given the relevance of these markets, not only from the investors’, but also from the issuers’ point of view (government and firms), it is necessary to fill this gap in the literature. In this paper, we study the sovereign market efficiency of thirty bond indices of both developed and emerging countries, using an innovative statistical tool in the financial literature: the complexity-entropy causality plane. This representation space allows us to establish an efficiency ranking of different markets and distinguish different bond market dynamics. We conclude that the classification derived from the complexity-entropy causality plane is consistent with the qualifications assigned by major rating companies to the sovereign instruments. Additionally, we find a correlation between permutation entropy, economic development and market size that could be of interest for policy makers and investors.     

The Spin-Echo Experiment and Statistical Mechanics

No Heading In this paper the spin-echo experiment is examined in the light of three different approaches to statistical mechanics: the coarse-graining Gibbsian approach, the interventionist Gibbsian approach, and the Boltzmannian approach. The conclusions of this examination are almost exactly opposite to the conclusions of Ridderbos and Redhead [1]: Firstly, it is argued that the spin-echo experiment does not tell against a coarse-graining approach to statistical mechanics. Secondly, it is argued that the interventionist approach to statistical mechanics is itself somewhat problematic as its statistical mechanical counterpart of thermodynamic entropy has a number of properties that actual thermodynamic entropy seemingly does not. In the final section of this paper a feature of coarse-grained entropies (their relativity) is noted that may enable coarse-graining approaches to reconcile conflicting intuitions about the behaviour of entropy in the spin-echo experiment, which may be considered a further advantage of such approaches.     

Phase maps of polycrystalline human biological fluids networks: statistical,correlation, and fractal analysis

The complex statistical and fractal analysis of phase properties, inherent to birefringence networks of liquid crystals consisting of optically-thin layers, prepared from synovial fluid taken from human joints, is performed in this work. Within the framework of a statistical approach, the authors have investigated values and ranges for changes of statistical moments of the 1-st to the 4-th orders that characterize coordinate distributions for phase shifts between orthogonal components of amplitudes inherent to laser radiation, transformed by synovial fluid layers, for human joints with various pathologies. The correlation criteria for differentiation of phase maps, describing pathologically changed liquid-crystal networks, have been ascertained. In the framework of the fractal approach, dimensions of self-similar coordinate phase distributions as well as features of transformation of logarithmic dependences for power spectra of these distributions for various types of human joint pathologies are determined.     

Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges

With the advent of single-cell RNA-sequencing (scRNA-seq), it is possible to measure the expression dynamics of genes at the single-cell level. Through scRNA-seq, a huge amount of expression data for several thousand(s) of genes over million(s) of cells are generated in a single experiment. Differential expression analysis is the primary downstream analysis of such data to identify gene markers for cell type detection and also provide inputs to other secondary analyses. Many statistical approaches for differential expression analysis have been reported in the literature. Therefore, we critically discuss the underlying statistical principles of the approaches and distinctly divide them into six major classes, i.e., generalized linear, generalized additive, Hurdle, mixture models, two-class parametric, and non-parametric approaches. We also succinctly discuss the limitations that are specific to each class of approaches, and how they are addressed by other subsequent classes of approach. A number of challenges are identified in this study that must be addressed to develop the next class of innovative approaches. Furthermore, we also emphasize the methodological challenges involved in differential expression analysis of scRNA-seq data that researchers must address to draw maximum benefit from this recent single-cell technology. This study will serve as a guide to genome researchers and experimental biologists to objectively select options for their analysis.     

逐事件分析中高阶矩统计起伏的消除

逐事件分析是目前高能重离子碰撞研究中受到广泛注意的方法.由于单个事件的粒子数有限,在运用这一方法时,首先要解决统计起伏的消除问题.在现有文献中只讨论过低阶矩(最高到三阶)统计起伏的消除.本文研究了高阶矩中统计起伏的消除,给出了普遍公式.     

高斯初始能量分布下自由电子激光饱和状态的统计理论求解

基于高增益自由电子激光饱和状态的统计物理方法,在不同的电子束相空间初始分布下对原有理论进行了发展和推广,借助于多重数值积分技术解决了粒子束一般动量初始分布带来的计算困难,并以更接近实际的高斯动量分布为例进行计算。作为对比验证编写高斯初始动量分布下的直接数值模拟程序,将高斯初始分布同水袋两种初始分布的暖电子束入射情形下自由电子激光达到饱和时系统的光强、聚束因子和粒子相空间分布均进行相应计算和比较。结果表明：数值模拟和统计计算结果相一致;在目前选取的参数范围内,两种初始分布的相应结果差异并不显著。     

Statistical Mechanics of Complex Systems for Pattern Identification

This paper presents a statistical mechanics concept for identification of behavioral patterns in complex systems based on measurements (e.g., time series data) of macroscopically observable parameters and their operational characteristics. The tools of statistical mechanics, which provide a link between the microscopic (i.e., detailed) and macroscopic (i.e., aggregated) properties of a complex system are used to capture the emerging information and to identify the quasi-stationary evolution of behavioral patterns. The underlying theory is built upon thermodynamic formalism of symbol sequences in the setting of a generalized Ising model (GIM) of lattice-spin systems. In this context, transfer matrix analysis facilitates construction of pattern vectors from observed sequences. The proposed concept is experimentally validated on a richly instrumented laboratory apparatus that is operated under oscillating load for identification of evolving microstructural changes in polycrystalline alloys. This work has been supported in part by the U.S. Army Research Office (ARO) under Grant No. W911NF-07-1-0376, by NASA under Cooperative Agreement No. NNX07AK49A, and by the U.S. Office of Naval Research (ONR) under Grant No. N00014-08-1-0380. Any opinions, findings and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the sponsoring agencies.