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I present and discuss a model for the free-for-all passenger boarding which is employed by some discount air carriers. The model is based on the principles of statistical mechanics where each seat in the aircraft has an associated energy which reflects the preferences of the population of air travelers. As each passenger enters the airplane they select their seats using Boltzmann statistics, proceed to that location, load their luggage, sit down, and the partition function seen by remaining passengers is modified to reflect this fact. I discuss the various model parameters and make qualitative comparisons of this passenger boarding model with models which involve assigned seats. This model can also be used to predict the probability that certain seats will be occupied at different times during the boarding process. These results may be of value to industry professionals as a useful description of this boarding method. However, it also has significant value as a pedagogical tool since it is a relatively unusual application of undergraduate level physics and it describes a situation with which many students and faculty may be familiar.

In this article, I have investigated statistical mechanics of a non-stretched elastica in two dimensional space using path integral method. In the calculation, the MKdV hierarchy naturally appeared as the equations including the temperature fluctuation.I have classified the moduli of the closed elastica in heat bath and summed the Boltzmann weight with the thermalfluctuation over the moduli. Due to the bilinearity of the energy functional,I have obtained its exact partition function.By investigation of the system,I conjectured that an expectation value at a critical point of this system obeys the Painlev\'e equation of the first kind and its related equations extended by the KdV hierarchy.Furthermore I also commented onthe relation between the MKdV hierarchy and BRS transformationin this system.

- This paper presents an experimental evaluation of physical human-human interaction in lightweight condition using a one degree of freedom robotized setup. It explores possible origins of Physical Human-Human communication, more precisely, the hypothesis of a time based communication. To explore if the communication is correlated to time a statistical state machine model based on physical Human-Human interaction is proposed. The model is tested with 14 subjects and presents results that are close to human-human performances.

The "finite-size" effects in the isobaric yield ratio (IYR), which are shown in the standard grand-canonical and canonical statistical ensembles (SGC/CSE) method, is claimed to prevent obtaining the actual values of physical parameters. The conclusion of SGC/CSE maybe questionable for neutron-rich nucleus induced reaction. To investigate whether the IYR has "finite-size" effects, the IYR for the mirror nuclei [IYR(m)] are reexamined using a modified statistical abrasion-ablation (SAA) model. It is found when the projectile is not so neutron-rich, the IYR(m) depends on the isospin of projectile, but the size dependence can not be excluded. In reactions induced by the very neutron-rich projectiles, contrary results to those of the SGC/CSE models are obtained, i.e., the dependence of the IYR(m) on the size and the isospin of the projectile is weakened and disappears both in the SAA and the experimental results.

The threshold network model is a type of finite random graphs. In this paper, we introduce a generalized threshold network model. A pair of vertices with random weights is connected by an edge when real-valued functions of the pair of weights belong to given Borel sets. We extend several known limit theorems for the number of prescribed subgraphs to show that the strong law of large numbers can be uniform convergence. We also prove two limit theorems for the local and global clustering coefficients.

We study local asymptotic properties of likelihood ratios of certain Heston models. We distinguish three cases: subcritical, critical and supercritical models. For the drift parameters, local asymptotic normality is proved in the subcritical case, only local asymptotic quadraticity is shown in the critical case, while in the supercritical case not even local asymptotic quadraticity holds. For certain submodels, local asymptotic normality is proved in the critical case, and local asymptotic mixed normality is shown in the supercritical case. As a consequence, asymptotically optimal (randomized) tests are constructed in cases of local asymptotic normality. Moreover, local asymptotic minimax bound, and hence, asymptotic efficiency in the convolution theorem sense are concluded for the maximum likelihood estimators in cases of local asymptotic mixed normality.

We present a formalism for computing the complexity of metastable states and the zero-temperature magnetic hysteresis loop in the soft-spin random-field model in finite dimensions. The complexity is obtained as the Legendre transform of the free-energy associated to a certain action in replica space and the hysteresis loop above the critical disorder is defined as the curve in the field-magnetization plane where the complexity vanishes; the nonequilibrium magnetization is therefore obtained without having to follow the dynamical evolution. We use approximations borrowed from condensed-matter theory and based on assumptions on the structure of the direct correlation functions (or proper vertices), such as a local approximation for the self-energies, to calculate the hysteresis loop in three dimensions, the correlation functions along the loop, and the second moment of the avalanche-size distribution.

Network science has been extensively developed to characterize structural properties of complex systems, including brain networks inferred from neuroimaging data. As a result of the inference process, networks estimated from experimentally obtained biological data, represent one instance of a larger number of realizations with similar intrinsic topology. A modeling approach is therefore needed to support statistical inference on the bottom-up local connectivity mechanisms influencing the formation of the estimated brain networks. We adopted a statistical model based on exponential random graphs (ERGM) to reproduce brain networks, or connectomes, estimated by spectral coherence between high-density electroencephalographic (EEG) signals. We validated this approach in a dataset of 108 healthy subjects during eyes-open (EO) and eyes-closed (EC) resting-state conditions. Results showed that the tendency to form triangles and stars, reflecting clustering and node centrality, better explained the global properties of the EEG connectomes as compared to other combinations of graph metrics. Synthetic networks generated by this model configuration replicated the characteristic differences found in brain networks, with EO eliciting significantly higher segregation in the alpha frequency band (8-13 Hz) as compared to EC. Furthermore, the fitted ERGM parameter values provided complementary information showing that clustering connections are significantly more represented from EC to EO in the alpha range, but also in the beta band (14-29 Hz), which is known to play a crucial role in cortical processing of visual input and externally oriented attention. These findings support the current view of the brain functional segregation and integration in terms of modules and hubs, and provide a statistical approach to extract new information on the (re)organizational mechanisms in healthy and diseased brains.

We propose a "social physics" model for two-group conflict. We consider two disputing groups. Each individual i in each of the two groups has a preference si regarding the way in which the conflict should be resolved. The individual preferences span a range between +M (prone to protracted conflict) and --M (prone to settle the conflict). The noise in this system is quantified by a "social temperature." Individuals interact within their group and with individuals of the other group. A pair of individuals (i, j) within a group contributes-si * sj to the energy. The inter-group energy of individual i is taken to be proportional to the product between si and the mean value of the preferences from the other group's members. We consider an equivalent-neighbor Renyi-Erdos network where everyone interacts with everyone. We present some examples of conflicts that may be described with this model. PACS numbers: 89.65.-s Social and economic system, 89.75.-k Complex systems, 05.90.+m Other topics in statistical physics, thermodynamics, and nonlinear dynamical systems

A dynamic rain attenuation prediction model is developed for use in obtaining the temporal characteristics, on time scales of minutes or hours, of satellite communication link availability. Analagous to the associated static rain attenuation model, which yields yearly attenuation predictions, this dynamic model is applicable at any location in the world that is characterized by the static rain attenuation statistics peculiar to the geometry of the satellite link and the rain statistics of the location. Such statistics are calculated by employing the formalism of Part I of this report. In fact, the dynamic model presented here is an extension of the static model and reduces to the static model in the appropriate limit. By assuming that rain attenuation is dynamically described by a first-order stochastic differential equation in time and that this random attenuation process is a Markov process, an expression for the associated transition probability is obtained by solving the related forward Kolmogorov equation. This transition probability is then used to obtain such temporal rain attenuation statistics as attenuation durations and allowable attenuation margins versus control system delay.

Unlike equilibrium statistical mechanics, with its well-established foundations, a similar widely-accepted framework for non-equilibrium statistical mechanics (NESM) remains elusive. Here, we review some of the many recent activities on NESM, focusing on some of the fundamental issues and general aspects. Using the language of stochastic Markov processes, we emphasize general properties of the evolution of configurational probabilities, as described by master equations. Of particular interest are systems in which the dynamics violate detailed balance, since such systems serve to model a wide variety of phenomena in nature. We next review two distinct approaches for investigating such problems. One approach focuses on models sufficiently simple to allow us to find exact, analytic, non-trivial results. We provide detailed mathematical analyses of a one-dimensional continuous-time lattice gas, the totally asymmetric exclusion process (TASEP). It is regarded as a paradigmatic model for NESM, much like the role the Ising model played for equilibrium statistical mechanics. It is also the starting point for the second approach, which attempts to include more realistic ingredients in order to be more applicable to systems in nature. Restricting ourselves to the area of biophysics and cellular biology, we review a number of models that are relevant for transport phenomena. Successes and limitations of these simple models are also highlighted.

The flux variability of blazars at very high energies does not have a clear origin. Flux variations on time scales down to the minute suggest that variability originates in the jet, where a relativistic boost can shorten the observed time scale, while the linear relation between the flux and its RMS or the skewness of the flux distribution suggests that the variability stems from multiplicative processes, which are associated in some models with the accretion disk. We study the RMS-flux relation and emphasize its link to Pareto distributions, characterized by a power-law probability density function. Such distributions are naturally generated within a minijets- in-a-jet statistical model, in which boosted emitting regions are isotropically oriented within the bulk relativistic flow of a jet. We prove that, within this model, the flux of a single minijet is proportional to its RMS. This relation still holds when considering a large number of emitting regions, for which the distribution of the total flux is skewed and could be interpreted as being log-normal. The minijets-in-a-jet statistical model reconciles the fast variations and the statistical properties of the flux of blazars at very high energies.

We suppose that overall strangeness production in both high energy elementary and heavy ion collisions can be described within the framework of an equilibrium statistical model in which the effective degrees of freedom are constituent quarks as used in effective lagrangian models. In this picture, the excess of relative strangeness production in heavy ion collisions with respect to elementary particle collisions arises from the unbalance between initial non-strange matter and antimatter and from the exact colour and flavour quantum number conservation over different finite volumes. The comparison with the data and the possible sources of model dependence are discussed.

Background: The analysis of DNA methylation is a key component in the development of personalized treatment approaches. A common way to measure DNA methylation is the calculation of beta values, which are bounded variables of the form M = (M + U) that are generated by Illumina's 450k BeadChip array. The statistical analysis of beta values is considered to be challenging, as traditional methods for the analysis of bounded variables, such as M-value regression and beta regression, are based on regularity assumptions that are often too strong to adequately describe the distribution of beta values. Results: We develop a statistical model for the analysis of beta values that is derived from a bivariate gamma distribution for the signal intensities M and U. By allowing for possible correlations between M and U, the proposed model explicitly takes into account the data-generating process underlying the calculation of beta values. Conclusion: The proposed model can be used to improve the identification of associations between beta values and covariates such as clinical variables and lifestyle factors in epigenome-wide association studies. It is as easy to apply to a sample of beta values as beta regression and M-value regression.

Part 1 of this series presented a Monte Carlo Bayesian method for constraining a complex statistical model of global circulation model (GCM) sub-gridcolumn moisture variability using high-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) cloud data, thereby permitting parameter estimation and cloud data assimilation for large-scale models. This article performs some basic testing of this new approach, verifying that it does indeed reduce mean and standard deviation biases significantly with respect to the assimilated MODIS cloud optical depth, brightness temperature and cloud-top pressure and that it also improves the simulated rotational-Raman scattering cloud optical centroid pressure (OCP) against independent (non-assimilated) retrievals from the Ozone Monitoring Instrument (OMI). Of particular interest, the Monte Carlo method does show skill in the especially difficult case where the background state is clear but cloudy observations exist. In traditional linearized data assimilation methods, a subsaturated background cannot produce clouds via any infinitesimal equilibrium perturbation, but the Monte Carlo approach allows non-gradient-based jumps into regions of non-zero cloud probability. In the example provided, the method is able to restore marine stratocumulus near the Californian coast, where the background state has a clear swath. This article also examines a number of algorithmic and physical sensitivities of the new method and provides guidance for its cost-effective implementation. One obvious difficulty for the method, and other cloud data assimilation methods as well, is the lack of information content in passive-radiometer-retrieved cloud observables on cloud vertical structure, beyond cloud-top pressure and optical thickness, thus necessitating strong dependence on the background vertical moisture structure. It is found that a simple flow-dependent correlation modification from Riishojgaard provides some help in this respect, by better honouring inversion structures in the background state.

We develop, evaluate, and apply SynthETC, a statistical-stochastic model of winter extra-tropical cyclones (ETCs) over eastern North America. SynthETC simulates the life cycle of ETCs from formation to termination, and it can be used to estimate the probability of extreme ETC events beyond the historical record. Two modes of climate variability are used as independent covariates: El NioSouthern Oscillation (ENSO) Nino3.4 and the monthly North Atlantic Oscillation (NAO). We use SynthETC to estimate the annual occurrence rate over sites in eastern North America of intense ETC passage in different ENSO and NAO states. Positive NAO is associated with increased rates over the North Atlantic, while negative NAO is associated with decreased rates over the North Atlantic and increased rates over northern Quebec. Positive ENSO is associated with decreased rates over the North Atlantic, Ontario, and the Canadian Maritime, while negative ENSO is associated with increased rates over those regions, as well as the Great Lakes region.

Statistical analysis of missile launching

Phase transitions occurring during electrode processes have been studied using a model in which the electrode is a planar wall with sticky adsorption sites. This model is used to explain the underpotential deposition (UPD) of copper on gold (111) in the presence of bisulfate ions. The model assumes that the bisulfate ions form a template for the adsorption of the first 2/3 of a monolayer of copper onto a honeycomb lattice. The centers of the hexagons that form the honeycomb are occupied by the bisulfate. In the absence of copper the bisulfate is desorbed as the electrode becomes more negatively charged, and for that reason the template 'melts' when the fraction of occupied sites drops below a certain critical value, which is estimated using the hard hexagon model of Baxter. We assume strong copper-bisulfate coadsorption, so that in the presence of a sufficiently large amount of Cu the template is reconstructed. Our model explains the qualitative features of the voltammogram, and makes definite predictions for the structure that should be observed.

This paper proposes a synthetic statistical top-down MIMO power line communications channel model based on a pure phenomenological approach. The basic idea consists of directly synthesizing the experimental channel statistical properties to obtain an extremely compact model that requires a small set of parameters. The model is derived from the analysis of the in-home 2 $\times$ 3 MIMO PLC channel data set obtained by the ETSI Specialist Task Force 410 measurement campaign in the band 1.8-100 MHz. The challenge of modeling the channel statistical correlation, exhibited among the frequencies and between the MIMO modes, in compact form is tackled and it is shown that a small set of parameters can be used to reconstruct such a correlation behavior. The model is validated and compared to the measured channels, showing a good agreement in terms of average channel gain, root-mean-square delay spread, coherence bandwidth, and channel capacity distribution.

Networks are often characterized by node heterogeneity for which nodes exhibit different degrees of interaction and link homophily for which nodes sharing common features tend to associate with each other. In this paper, we propose a new directed network model to capture the former via node-specific parametrization and the latter by incorporating covariates. In particular, this model quantifies the extent of heterogeneity in terms of outgoingness and incomingness of each node by different parameters, thus allowing the number of heterogeneity parameters to be twice the number of nodes. We study the maximum likelihood estimation of the model and establish the uniform consistency and asymptotic normality of the resulting estimators. Numerical studies demonstrate our theoretical findings and a data analysis confirms the usefulness of our model.

The dynamic and composite nature of propagation impairments that are incurred on Earth-space communications links at frequencies in and above 30/20 GHz Ka band, i.e., rain attenuation, cloud and/or clear air scintillation, etc., combined with the need to counter such degradations after the small link margins have been exceeded, necessitate the use of dynamic statistical identification and prediction processing of the fading signal in order to optimally estimate and predict the levels of each of the deleterious attenuation components. Such requirements are being met in NASA's Advanced Communications Technology Satellite (ACTS) Project by the implementation of optimal processing schemes derived through the use of the Rain Attenuation Prediction Model and nonlinear Markov filtering theory.

Printing, photocopying and scanning processes degrade the image quality of a document. Statistical models of these degradation processes are crucial for document image understanding research. Models allow us to predict system performance; conduct controlled experiments to study the break-down points of the systems; create large multi-lingual data sets with ground truth for training classifiers; design optimal noise removal algorithms; choose values for the free parameters of the algorithms; and so on. Although research in document understanding started many decades ago, only two document degradation models have been proposed this far. Furthermore, no attempts have been to statistically validate these models. In this paper we present a statistical methodology that can be used to validate local degradation models. This method is based on a non-parametric, two-sample permutation test. Another standard statistical device - the power function - is then used to choose between algorithm variables such as distance functions. Since the validation and the power function procedures are independent of the model, they can be used to validate any other degradation model. A method for comparing any two models is also described. It uses p-values associated with the estimated models to select the model that is closer to the real world.

The structure and time development of the magnetosphere-ionosphere system have significant impacts on the Air Force and its mission. Specifically, an accurate knowledge of ionospheric plasma densities is important for the operation of many Air Force systems. This research analyzes plasma density structure development through comparing two distinct electric field models. The two models compared here are a commonly used statistical model created by Heppner and Maynard 1987, and a more recently developed model using real-time coherent radar measurements from the SuperDARN radar network. Ionospheric simulations were run using Utah State University s Time-Dependent Ionospheric Model (TDIM) with the two electric field models as drivers, and density results from the simulations were compared with both a conceptual model and in-situ DMSP satellite measurements. While there are limitations to the comparison technique, results indicate that, in general, using the SuperDARN-derived electric fields to drive the TDIM has advantages over using the statistical fields. The higher spatial and temporal resolution of the input electric fields generally seem to produce more realistic morphological density structures, with smoothing due to statistical averaging and geomagnetic index-binning reduced. This research provides an essential first step in using high resolution, real-time SuperDARN-derived electric fields to drive a physical model of the ionosphere in order to create realistic ionospheric density results.

A new statistical model for western North Pacific Ocean tropical cyclone genesis and tracks is developed and applied to estimate regionally resolved tropical cyclone landfall rates along the coasts of the Asian mainland, Japan, and the Philippines. The model is constructed on International Best Track Archive for Climate Stewardship (IBTrACS) 1945-2007 historical data for the western North Pacific. The model is evaluated in several ways, including comparing the stochastic spread in simulated landfall rates with historic landfall rates. Although certain biases have been detected, overall the model performs well on the diagnostic tests, for example, reproducing well the geographic distribution of landfall rates. Western North Pacific cyclogenesis is influenced by El Nino-Southern Oscillation (ENSO). This dependence is incorporated in the model s genesis component to project the ENSO-genesis dependence onto landfall rates. There is a pronounced shift southeastward in cyclogenesis and a small but significant reduction in basinwide annual counts with increasing ENSO index value. On almost all regions of coast, landfall rates are significantly higher in a negative ENSO state (La Nina).

The purpose of this research effort is to develop, simulate, and test a new algorithm to detect Near Earth Objects (NEOs) using a Likelihood Ratio Test (LRT) based on a Poisson statistical model for the arrival of photons. One detection algorithm currently in use is based on a Gaussian approximation of the arrival of photons, and is compared to the proposed Poisson model. The research includes three key components. The first is a quantitative analysis of the performance of both algorithms. The second is a system model for simulating detection statistics. The last component is a collection of measured data to apply comparatively to both algorithms. A Congressional mandate directs NASA and the DoD to catalogue 90% of all NEOs by the year 2020. [1] Results from this research effort could feasibly be applied directly to operations in the Pan-Starrs program to facilitate the accomplishment of the Congressional mandate. Improvements in the size of detectable NEOs and in the probability of detecting larger NEOs would increase the state of readiness of the world for possible catastrophic impact events. Improvements in detection probability of measured data were as high as a factor of seven, and the expected average improvement is around 10%.

We present a rigorous statistical-mechanics theory of nonlinear many mode laser systems. An important example is the passively mode-locked laser that promotes pulse operation when a saturable absorber is placed in the cavity. It was shown by Gordon and Fischer [1] that pulse formation is a first-order phase transition of spontaneous ordering of modes in an effective "thermodynamic" system, in which intracavity noise level is the effective temperature. In this paper we present a rigorous solution of a model of passive mode locking. We show that the thermodynamics depends on a single parameter, and calculate exactly the mode-locking point. We find the phase diagram and calculate statistical quantities, including the dependence of the intracavity power on the gain saturation function, and finite size corrections near the transition point. We show that the thermodynamics is independent of the gain saturation mechanism and that it is correctly reproduced by a mean field calculation. The outcome is a new solvable statistical mechanics system with an unstable self-interaction accompanied by a natural global power constraint, and an exact description of an important many mode laser system.

Some aspects of a recent paper about protein-membrane interaction are inadequate due to lack of structural information.

Mathematical model for statistical probability of internal microbial spacecraft contamination

A computer program has been tested and documented (Warnock and VanZandt, 1985) that estimates mean values of the refractivity turbulence structure constant in the stable free atmosphere from standard National Weather Service balloon data or an equivalent data set. The program is based on the statistical model for the occurrence of turbulence developed by VanZandt et al. (1981). Height profiles of the estimated refractivity turbulence structure constant agree well with profiles measured by the Sunset radar with a height resolution of about 1 km. The program also estimates the energy dissipation rate (epsilon), but because of the lack of suitable observations of epsilon, the model for epsilon has not yet been evaluated sufficiently to be used in routine applications. Vertical profiles of the refractivity turbulence structure constant were compared with profiles measured by both radar and optical remote sensors and good agreement was found. However, at times the scintillometer measurements were less than both the radar and model values.

Atmospheric Carbon Monoxide (CO) provides a window on the chemistry of the atmosphere since it is one of few chemical constituents that can be remotely sensed, and it can be used to determine budgets of other greenhouse gases such as ozone and OH radicals. Remote sensing platforms in geostationary Earth orbit will soon provide regional observations of CO at several vertical layers with high spatial and temporal resolution. However, cloudy locations cannot be observed and estimates of the complete CO concentration fields have to be estimated based on the cloud-free observations. The current state-of-the-art solution of this interpolation problem is to combine cloud-free observations with prior information, computed by a deterministic physical model, which might introduce uncertainties that do not derive from data. While sharing features with the physical model, this paper suggests a Bayesian hierarchical model to estimate the complete CO concentration fields. The paper also provides a direct comparison to state-of-the-art methods. To our knowledge, such a model and comparison have not been considered before.

Drought is a common, natural climate phenomenon on the earth with various effects on the economic, social, and environmental aspects of life. The impact of drought on agriculture, water access, and socioeconomic activities of the region depends on various physiological, environmental, and social factors. Due to declined rainfall in specific and efficient periods, drought could lead to water scarcity. Hydrological drought is defined as the reduction of available water in all its forms (Ma, Ren, Singh, Yuan, Chen, Yang, & Liu, 2016). Climate change could influence the frequency and severity of droughts. Therefore, drought vulnerability depends on several factors, such as topographic features, development of soil, land, and water resources, groundwater utilization, and the regional water demand for domestic, industrial, and agricultural activities. Since these impacts may be local or regional, it is difficult to evaluate the effects of drought on various sectors. Risk is often recognized as the combined probability of a specific event (e.g., drought) and its negative consequences. Risk is defined as the endangerment of a system, which could often be the environment, where drought is interpreted as an environmental risk. The response of the environment to a hazard depends on the severity of the hazard and environmental properties, which could be classified into four categories of economic, social, physical, and environmental factors. The previous studies in this regard have mainly introduced economic and social factors as the risk factors for the occurrence of droughts. In the aforementioned studies, field questionnaires have not been used to assess drought vulnerability, while in the present study, the main research instrument was a questionnaire to estimate drought vulnerability. The present study aimed to assess the risk status of drought in a region in Iran based on observational data and the climatic downscaled data obtained from the coordinated regional climate downscaling experiment (CORDEX) project in order to predict the risk of drought. Study Area Afin sub-basin covers an area of 655 square kilometers. The Afrin basin is between 33 degrees and 42 minutes north to 33 degrees and 58 minutes north and 52 degrees 35 minutes east to 53 degrees and 31 minutes north. Khoshbakht river is the permanent river of this basin and part of a larger sub-basin known as Esfedan, which is connected to Zuzan (northeast) and Khaf salt pan. It is also connected to Qaen (northwest), Sharokht (southeast), and Saddeh and Birjand (southwest). The average precipitation in Afin sub-basin is approximately 156.4 millimeters, and the average temperature in the region is 14.7°C (Figure 1). Based on the climate classification methods proposed by Amberje, the climate of Afin sub-basin is arid-cold. The arid-cold climate of the area is suitable for the growth of barberries. The economy of the village in the vicinity of Afin sub-basin has historically relied on agriculture, especially the cultivation of barberries. In fact, most of Iran's barberry is produced in this region. Materials and Methods At the first stage, the standardized precipitation evapotranspiration index (SPEI), effective reconnaissance drought index (eRDI), and standardized runoff index (SRI) were applied as the hydrological indices in this regard. The base period was the year 1983, and the utilized data included the monthly data on the average maximum temperature, average minimum temperature, mean surface air temperature, precipitation, and river flow data of Khoshbakht River in Afin sub-basin. Drought Hazard is defined as the probability of drought occurring at various levels of severity. In the current research, the probability of drought occurrence, and the probability of drought occurrence in each drought class was calculated using the normal z-score table. Vulnerability is a set of economic, social, and environmental conditions, which determine the sensitivity of an environment to risk. In the present study, a questionnaire consisting of 35 items was prepared in order to identify the extent of the impact of each vulnerability factor, and exploratory factor analysis and confirmatory factor analysis were applied to determine the vulnerability level of the selected area. Risk is defined as the result of the interactions between a system (environment), natural hazards, and socioeconomic vulnerability relative to the risk. The level of drought risk could be estimated after calculating the drought hazard and its vulnerability based on the definition of risk. In order to access the required meteorological data in the future (as mentioned in the Methodology section) and due to the level of access to the CORDEX project data, only three models of GFDL-ESM2M, CNRM-CERFAC, and ICHEC-EC-EARTH were selected in the current research since all the required meteorological parameters must have historical data, RCP4.5, and RCP8.5. In the present study, the IHACRES model was utilized to obtain the future runoff data. Since the vulnerability level obtained by the questionnaire was considered constant over time in the modeling of drought risk, hazard modeling was carried out. After the prediction of the hazard, future drought risk was estimated by multiplying the vulnerability value as described in the previous section. Two methods were exploited for the modeling of the drought risk, including the bootstrapped quantile regression (500 repetitions) and loess nonparametric regression. Results and Discussion Correlation-coefficient (R), explanation coefficient (R2), and Nash-Sutcliffe coefficient (NS) were used to assess the predictive ability of the climate models. After correction, the combined model for the precipitation and temperature parameters was determined using the weighted average method and Bayesian approach. According to the obtained results, the ICHEC-EARTH model had the highest coefficient as an integrated model based on the RCP4.5 scenario.  Based on the two scenarios, the amount of runoff will have no significant changes in the upcoming years. Increased temperature and the subsequent probability of excessive rainfall in the studied area may be the reason for the lack of runoff in the upcoming years. Based on the completed questionnaires, the vulnerability score was 0.53 for Afin sub-basin, which indicated that about half of the sub-basin vulnerability was due to the economic, social, and environmental factors of the sub-basin. Furthermore, the vulnerability value was considered to be a mediator of the sensitivity of the mentioned factors. According to the results based on the mentioned meteorological drought indices, most variations in the drought risk were observed in winter. The results of drought risk assessment indicated that most variations in the frequency of droughts occurred in winter compared to the base season. On the other hand, the SRI findings showed no significant risk of severe droughts in any of the time scales within the next three periods based on the RCP4.5 and RCP8.5 scenarios. Conclusion The present study aimed to provide a model to predict the drought risk status in the future in the form of a case study of Afin sub-basin in Iran based on the meteorological drought indices of SPEI and eRDI and hydrological drought index of SRI. In general, the obtained results could be summarized as follows: SPEI and eRDI had the same performance in estimating the frequency of drought events, while eRDI showed higher drought intensity compared to SPEI, which could be due to the use of effective precipitation as an input in this index. In addition, SPI and eRDI indicated approximately equal drought severity. A slight increase was observed in the runoff of the future years based on the RCP8.5 scenario. Based on the meteorological drought indices, the possibility of highly severe droughts within the next 27 years is lower compared to the mid and far future periods. The meteorological drought indices showed that most drought changes in the investigated time scales occur in winter.

In this first of a multi-step analysis we test whether, D, a novel metric of structural complexity, predicts the fitting propensity of structural equation models.

Monitoring and feedback systems could potentially support therapists to better recognize and adequately respond to alliance ruptures in the therapeutic process. To enable this, the objective of this study is to investigate whether alliance ruptures (withdrawal and confrontation) can be reliably detected by statistical prediction models based on routinely collected data from psychotherapeutic care. Data collection is carried out in a naturalistic setting at an outpatient center for psychotherapy at the University of Greifswald. Measurements in the form of short questionnaires are taken before and after each therapy session. In addition, video recordings of these sessions are evaluated using the Rupture Resolution Rating System (3RS; Eubanks & Muran, 2022). Based on these data, an algorithm will be created to predict the occurrence of alliance ruptures by using an ensemble of different machine learning methods („Super Learner“; van der Laan et al., 2007).

Thesis (M.S. in O.R.)--U.S. Naval Postgraduate School, 1965

In randomized control trials (RCTs) of educational interventions, there is a growing literature on impact estimation methods to adjust for missing student outcome data using such methods as multiple imputation, the construction of nonresponse weights, casewise deletion, and maximum likelihood methods (see, for example, Allison, 2002; Graham, 2009; Peugh & Enders, 2004; Puma, Olsen, Bell & Price, 2009; Schafer & Graham, 2002). Much less attention, however, has been devoted in education RCTs to developing statistical methods to adjust for the systematic misreporting of student outcome data for those with nonmissing data. Without appropriate adjustments, misreporting could lead to biased impact estimates, which could be exacerbated if the intervention leads to treatment-control differences in misreporting rates and the composition of students with misreported data. Misreporting could also affect the variance of the estimated impacts, and hence, significance levels from statistical hypothesis tests of intervention effects. This article develops a parametric statistical framework to test and adjust for the misreporting of binary outcomes in the estimation of average treatment effects (ATEs) for school-based RCTs. The author considers a realistic scenario where it is assumed that binary outcomes on sensitive topics can be misreported for students with truly undesirable outcomes, but not for those with truly desirable outcomes. A latent index framework is employed where misreporting and binary outcome decision processes are modeled using available baseline data and normality assumptions about model error terms. This approach yields a "double hurdle" random effects probit model that can be estimated separately for treatments and controls. The article discusses quasi-Newton ML methods for obtaining consistent estimates of the unobserved misreporting rates, the ATEs on the considered binary outcomes, and standard errors of the estimates that are not contaminated by misreporting. The article also discusses how the approach can be applied to continuous outcomes and to nonclustered, student-level RCT designs

The critical exponent of susceptibility is expected as 1 in systems belonging to the mean-field universality class. On the other hand, the mean-field nature permits to introduce the Vlasov equation in the place of the Liouville equation, and the linear response theory based on the Vlasov equation gives the strange exponent 1/4 in the low-temperature side of the Hamiltonian mean-field model. We clarify that this strange exponent is due to existence of Casimir invariants which traps the system in a quasistationary state for a time scale diverging with the system size. The theoretical prediction is numerically confirmed by N-body simulations and numerical Vlasov solutions.

In this paper we develop a new statistical model for the irradiance fluctuations of an unbounded optical wavefront (plane and spherical waves) propagating through a turbulent medium under all irradiance fluctuation conditions in homogeneous, isotropic turbulence. The major advantage of the model is that leads to closed-form and mathematically-tractable expressions for the fundamental channel statistics of an unbounded optical wavefront under all turbulent regimes. Furthermore, it unifies most of the proposed statistical models for the irradiance fluctuations derived in the bibliography providing, in addition, an excellent agreement with the experimental data.

Change detection plays an important role in most video-based applications. The first stage is to build appropriate background model, which is now becoming increasingly complex as more sophisticated statistical approaches are introduced to cover challenging situations and provide reliable detection. This paper reports a simple and intuitive statistical model based on deeper learning spatial correlation among pixels: For each observed pixel, we select a group of supporting pixels with high correlation, and then use a single Gaussian to model the intensity deviations between the observed pixel and the supporting ones. In addition, a multi-channel model updating is integrated on-line and a temporal intensity constraint for each pixel is defined. Although this method is mainly designed for coping with sudden illumination changes, experimental results using all the video sequences provided on changedetection.net validate it is comparable with other recent methods under various situations.

The design and operation of simulation models for studying management policies and other problems that involve complex systems of random variables are being studied. The present text is a companion piece to M. A. Geisler, The Sizes of Simulation Samples Required to Compute Certain Inventory Characteristics with Stated Precision and Confidence, The RAND Corporation, RM-3242-PR, October, 1962. Special statistical methods were used in that study to compute the sample sizes for specified inventory models. In this study, the methods are tested by applying them to particular inventory cases, and determining how well the actual precision and confidence obtained in the estimates agreed with expectation.

A representative midsize-male foot was generated via a statistical analysis of foot scans from 107 men with widely varying body size. Seventy-two surface landmarkswere manually extracted from the original scan data. A template fitting method was used to represent each scan with a homologous mesh. A principal component analysis and least-squares linear regression were used to generate a foot surface model with landmarks using a reference stature of 1755 mm and a body mass of 83.19 kg. The statistical model can be used to generate a wide range of male foot sizes and shapes.

Atmospheric effects on commercial and military air platforms are of concern and must be considered for flight planning or military mission execution. Atmospheric models exist that can forecast adverse weather, thus mitigating the impacts on operations. The Weather Research and Forecasting (WRF) model is a numerical weather prediction system designed for operational forecasting and atmospheric research. This report examined WRF model output and investigated any potential value-added for higher-resolution model grids using statistical methods. As a supplement to a Phase 1 of analyses conducted by the lead author of this report, Phase 2 examines additional atmospheric variables beyond surface temperature and then performs a location-based analysis to assess model performance. The model performs best for surface temperature and upper-level winds. Surface temperature was selected as the primary variable for this study. The analysis separated the observation station data into three categories: Valleys, Plains, and Mountains. Results showed significant improvement of correlation coefficients when compared to the Phase 1 effort, but the higher-resolution 1-km results showed little improvement compared to the lower-resolution 3-km model results, suggesting that examination of more data is in order.

Some wireless channel models based on probability density functions have been proposed, including models based on Markov chains. However, they are limited to a certain number of perturbations. This paper proposes a methodology based in a Ku‐band signal behavior which is classified in three cases: ascendant, descendent and constant. Samples are classified in these cases so their conditional probabilities are analyzed in order to find a fit to a probability density function and to extract its statistical parameters which are the model itself. From this information, a new signal was generated and their second order statistics are compared with the ones from the original signal to validate the created model. The analyzed signal was extracted from a measurement campaign done in Mexico.

A simple statistical model in terms of light-front kinematic variables is used to explain the nuclear EMC effect in the range $x \in [0.2,~0.7]$, which was constructed by us previously to calculate the parton distribution functions (PDFs) of the nucleon. Here, we treat the temperature $T$ as a parameter of the atomic number $A$, and get reasonable results in agreement with the experimental data. Our results show that the larger $A$, the lower $T$ thus the bigger volume $V$, and these features are consistent with other models. Moreover, we give the predictions of the quark distribution ratios, \emph{i.e.}, $q^A(x) / q^D(x)$, $\bar{q}^A(x) / \bar{q}^D(x)$, and $s^A(x) / s^D(x)$, and also the gluon ratio $g^A(x) / g^D(x)$ for iron as an example. The predictions are different from those by other models, thus experiments aiming at measuring the parton ratios of antiquarks, strange quarks, and gluons can provide a discrimination of different models.

A statistical gravity field model has been developed for northern Texas, where the Gravity Gradiometer Survey System (GGSS) will be tested. The model is described in this report using both round-earth and flat-earth Attenuated White Noise (AWN) formulations. The model is fitted to all worldwide and local gravity-field information that is applicable to the GGSS test site. In addition, a discrete approximation to the AWN model is described, which can be used to compute simulated GGSS survey data and the surface truth data for the test area. It is expected that the AWN model and the derived simulated data will be useful for optimizing, testing, and validating GGSS data processing algorithms and software. Keywords: Covariance function; Mass dipoles. (Author)

In 1866 Gregor Mendel published a seminal paper containing the foundations of modern genetics. In 1936 Ronald Fisher published a statistical analysis of Mendel's data concluding that "the data of most, if not all, of the experiments have been falsified so as to agree closely with Mendel's expectations." The accusation gave rise to a controversy which has reached the present time. There are reasonable grounds to assume that a certain unconscious bias was systematically introduced in Mendel's experimentation. Based on this assumption, a probability model that fits Mendel's data and does not offend Fisher's analysis is given. This reconciliation model may well be the end of the Mendel--Fisher controversy.

In this paper we use a recently compiled data set, which comprises 118 galactic-scale strong gravitational lensing (SGL) systems to constrain the statistic property of SGL system, as well as the curvature of universe without assuming any fiducial cosmological model. Based on the singular isothermal ellipsoid (SIE) model of SGL system, we obtain that the constrained curvature parameter $\Omega_{\rm k}$ is close to zero from the SGL data, which is consistent with the latest result of planck measurement. More interestingly, we find that the parameter $f$ in the SIE model is strongly correlated with the curvature $\Omega_{\rm k}$. Neglecting this correlation in the analysis will significantly overestimate the constraining power of SGL data on the curvature. Furthermore, the obtained constraint on $f$ is different from previous results: $f=1.105\pm0.030$ ($68\%$ C.L.), which means that the standard singular isothermal sphere (SIS) model ($f=1$) is disfavored by the current SGL data at more than $3\sigma$ confidence level. We also divide the whole SGL data into two parts according to the centric stellar velocity dispersion $\sigma_{\rm c}$ and find that the larger value of $\sigma_{\rm c}$ the subsample has, the more favored the standard SIS model is. Finally, we extend the SIE model by assuming the power-law density profiles for the total mass density, $\rho=\rho_0(r/r_0)^{-\alpha}$, and luminosity density, $\nu=\nu_0(r/r_0)^{-\delta}$, and obtain the constraints on the power-law indexes: $\alpha=1.95\pm0.04$ and $\delta=2.40\pm0.13$ at 68\% confidence level. When assuming the power-law index $\alpha=\delta=\gamma$, this scenario is totally disfavored by the current SGL data, $\chi^2_{\rm min,\gamma} - \chi^2_{\rm min,SIE} \simeq 53$.

Motivation for the resurgence of interest in improving HF communication is presented. This includes the continued widespread use of the HF band, and the new technology that now makes it feasible to vastly improve the historically poor quality of communications in this band. Non-conventional HF techniques or systems are classified into four general categories according to the technical specialties that spawned them. These categories are Adaptive Frequency Management. Digital Waveform Processing, Networking, and Adaptive Antennas. A 15 parameter channel model is presented which forms the basis for the on-going RADC measurement program. These parameters address the dispersion and dynamics of time, frequency and spatial distortion imposed by the skywave channel. Next, measurement techniques are evaluated for characterization of these parameters, resulting in the selection of a six station Arctic network of wideband pulse compression (matched filter) channel probes. A description of waveform generation, receiver signal processing and the program plans and schedule are presented.

Transverse energy and charged particle pseudorapidity densities at midrapidity and their ratio, $dE_{T}/d\eta\mid_{mid} / dN_{ch}/d\eta|_{mid}$, are evaluated in a statistical model with longitudinal and transverse flows for the wide range of colliders, from AGS to RHIC at $\sqrt{s_{NN}}=200$ GeV. Evaluations are done at freeze-out parameters established independently from fits to observed particle yields and $p_{T}$ spectra. Decays of hadron resonances are treated thoroughly and are included in derivations of $dE_{T}/d\eta|_{mid}$ and $dN_{ch}/d\eta|_{mid}$. The predictions of the model agree well with the experimental data. However, some (explicable) overestimation of the ratio has been observed.

We describe how to consistently incorporate solar model uncertainties, along with experimental errors and correlations, when analyzing solar neutrino data to derive confidence limits on parameter space for proposed solutions of the solar neutrino problem. Our work resolves ambiguities and inconsistencies in the previous literature. As an application of our methods we calculate the masses and mixing angles allowed by the current data for the proposed MSW solution using both Bayesian and frequentist methods, allowing purely for solar model flux variations, to compare with previous work. We consider the effects of including metal diffusion in the solar models and also discuss implications for future experiments.

We briefly describe two statistical hadronization models, based respectively on the presence and absence of light quark chemical equilibrium, used to analyze particle yields in heavy ion collisions. We then try to distinguish between these models using $K/\pi$ fluctuations data. We find that while the non-equilibrium model provides an acceptable description of fluctuations at top SPS and RHIC energies, both models considerably under-estimate fluctuations at low SPS energies.