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We propose a method for assessing the accuracy of pseudo-random number sampling methods for evacuation modelling purposes. It consists of a systematic comparison between experimental and generated distributions. The calculated weighted relative error (Ew_rel) is based on the statistical parameters as central moments (mean, standard deviation, skewness and kurtosis) to shape the distribution. The case study involves the Box–Muller transform, the Kernel-Epanechnikov, the Kernel-Gaussian and the Piecewise linear generating samples from eight evacuation datasets fitted against normal, lognormal and uniform distributions. Keeping in mind that the Bos Muller method has two potential sources of error (i.e. distribution fitting and sampling), this method produces plausible results when generating samples from the three types of distributions (Ew_rel < 0.30 for normal, lognormal and uniform distributions). We also fund that the Kernel Gaussian and the Kernel Epanechnikov methods are well accurate in generating samples from normal distributions (Ew_rel < 0.1) but potentially inaccurate when generating samples from uniform and lognormal distributions (Ew_rel > 0.80). Results suggest that the Piecewise linear is the most accurate method (Ew_rel = 0.01 normal; Ew_rel = 0.04 lognormal; Ew_rel = 0.009 uniform). This method has the advantage of sampling directly from empirical datasets i.e. no previous distribution fitting is needed. While the proposed method is used here for evacuation modelling, it can be extended to other fire safety engineering applications.

The intrinsic variability of switching behavior in memristors has been a major obstacle to their adoption as the next generation of universal memory. On the other hand, this natural stochasticity can be valuable for hardware security applications. Here we propose and demonstrate a novel true random number generator utilizing the stochastic delay time of threshold switching in a Ag:SiO 2 diffusive memristor, which exhibits evident advantages in scalability, circuit complexity, and power consumption. The random bits generated by the diffusive memristor true random number generator pass all 15 NIST randomness tests without any post-processing, a first for memristive-switching true random number generators. Based on nanoparticle dynamic simulation and analytical estimates, we attribute the stochasticity in delay time to the probabilistic process by which Ag particles detach from a Ag reservoir. This work paves the way for memristors in hardware security applications for the era of the Internet of Things. Memristors can switch between high and low electrical-resistance states, but the switching behaviour can be unpredictable. Here, the authors harness this unpredictability to develop a memristor-based true random number generator that uses the stochastic delay time of threshold switching

Selection of a representative sampling area for accurate and reliable yield evaluation of wheat (Triticum aestivum L.) is important for optimizing straw retention in soils and its removal as biofuel feedstock. A field experiment was conducted at four wheat fields in the North China in 2020. Wheat straw and grain yields varied from 359.2-682.8 and 338.5-640.4 g [m.sup.2], respectively, and plant height varied from 51.3-59.7 cm across all plots in the four sites. Variation in either relative deviation (RD) or standard error (SE) of straw and grain yield and plant height estimates decreased with the increase in random sampling square (RSS) (one square = approximately 1 [m.sup.2]) and random sampling plant (RSP) numbers, respectively. Minimum RSS numbers of 3-10 and 1-10 [m.sup.2] were needed to satisfy RD less than 5% in two-third of the plots for straw and grain yield estimates, respectively. This suggests that 10 [m.sup.2] could be recommended as the minimum RSS number per plot. However, the incidence frequency of RD was 63.75% and 60.00% within the RD interval of 0%-5% for straw and grain yield estimates, respectively, from the RSS number of 10 [m..sup.2], indicating that yields from RSSs in field trials are prone to large variations. Therefore, it is strongly recommended to design a large plot as possible and to harvest the whole plot for estimating yields. The threshold RSP number ranged from 14-18 to satisfy RD less than 1.5% and a minimum RSP of 20 plants (including mains and tillers) per plot could be recommended for wheat field experiments.

The application for random numbers is ubiquitous. We experimentally build a well-studied quantum random number generator from homodyne measurements on the quadratures of the vacuum fluctuations. Semi-device-independence in this random number generator is usually obtained using phase modulators to shift the phase of the laser and obtain random sampling from both X and P quadrature measurements of the vacuum state in previous implementations. We characterize the experimental parameters for optimal performance of this source-device independent quantum random number generator by measuring the two quadratures concurrently using two homodyne detectors. We also study the influence of these parameters on randomness, which can be extracted based on Shannon entropy and von Neumann entropy, which correspond to an eavesdropper listening to classical and quantum side information, respectively.

Taylor’s law (TL), a widely verified quantitative pattern in ecology and other sciences, describes the variance in a species’ population density (or other nonnegative quantity) as a power-law function of the mean density (or other nonnegative quantity): Approximately, variance =a(mean) b ,a> 0. Multiple mechanisms have been proposed to explain and interpret TL. Here, we show analytically that observations randomly sampled in blocks from any skewed frequency distribution with four finite moments give rise to TL. We do not claim this is the only way TL arises. We give approximate formulae for the TL parameters and their uncertainty. In computer simulations and an empirical example using basal area densities of red oak trees from Black Rock Forest, our formulae agree with the estimates obtained by least-squares regression. Our results show that the correlated sampling variation of the mean and variance of skewed distributions is statistically sufficient to explain TL under random sampling, without the intervention of any biological or behavioral mechanisms. This finding connects TL with the underlying distribution of population density (or other nonnegative quantity) and provides a baseline against which more complex mechanisms of TL can be compared.

This paper introduces a new method for performing computational inference on log-Gaussian Cox processes. The likelihood is approximated directly by making use of a continuously specified Gaussian random field. We show that for sufficiently smooth Gaussian random field prior distributions, the approximation can converge with arbitrarily high order, whereas an approximation based on a counting process on a partition of the domain achieves only first-order convergence. The results improve upon the general theory of convergence for stochastic partial differential equation models introduced by Lindgren et al. (2011). The new method is demonstrated on a standard point pattern dataset, and two interesting extensions to the classical log-Gaussian Cox process framework are discussed. The first extension considers variable sampling effort throughout the observation window and implements the method of Chakraborty et al. (2011). The second extension constructs a log-Gaussian Cox process on the world's oceans. The analysis is performed using integrated nested Laplace approximation for fast approximate inference.

Consider the heteroscedastic nonparametric regression model with random design Yi = f(Xi ) + V 1/2(Xi )εi , i = 1,2,...,n, with f(·) and V(·) α- and β-Hölder smooth, respectively. We show that the minimax rate of estimating V(·) under both local and global squared risks is of the order n − 8 α β 4 α β + 2 α + β ∨ n − 2 β 2 β + 1 , where a ∨ b := max{a, b} for any two real numbers a, b. This result extends the fixed design rate n −4α ∨ n −2β/(2β+1) derived in (Ann. Statist. 36 (2008) 646–664) in a nontrivial manner, as indicated by the appearances of both α and β in the first term. In the special case of constant variance, we show that the minimax rate is n −8α/(4α+1) ∨ n −1 for variance estimation, which further implies the same rate for quadratic functional estimation and thus unifies the minimax rate under the nonparametric regression model with those under the density model and the white noise model. To achieve the minimax rate, we develop a U-statistic-based local polynomial estimator and a lower bound that is constructed over a specified distribution family of randomness designed for both εi and Xi .

Psychological scientists use statistical information to determine the workings of human behavior. We argue that young children do so as well. Over the course of a few years, children progress from viewing human actions as intentional and goal directed to reasoning about the psychological causes underlying such actions. Here, we show that preschoolers and 20-month-old infants can use statistical information—namely, a violation of random sampling—to infer that an agent is expressing a preference for one type of toy instead of another type of toy. Children saw a person remove five toys of one type from a container of toys. Preschoolers and infants inferred that the person had a preference for that type of toy when there was a mismatch between the sampled toys and the population of toys in the box. Mere outcome consistency, time spent with the toys, and positive attention toward the toys did not lead children to infer a preference. These findings provide an important demonstration of how statistical learning could underpin the rapid acquisition of early psychological knowledge.

Stated choice (SC) experiments represent the dominant data paradigm in the study of behavioral responses of individuals, households as well as other organizations, yet in the past little has been known about the sample size requirements for models estimated from such data. Traditional orthogonal designs and existing sampling theories does not adequately address the issue and hence researchers have had to resort to simple rules of thumb or ignore the issue and collect samples of arbitrary size, hoping that the sample is sufficiently large enough to produce reliable parameter estimates, or are forced to make assumptions about the data that are unlikely to hold in practice. In this paper, we demonstrate how a recently proposed sample size computation can be used to generate so-called S -efficient designs using prior parameter values to estimate panel mixed multinomial logit models. Sample size requirements for such designs in SC studies are investigated. In a numerical case study is shown that a D -efficient and even more an S -efficient design require a (much) smaller sample size than a random orthogonal design in order to estimate all parameters at the level of statistical significance. Furthermore, it is shown that wide level range has a significant positive influence on the efficiency of the design and therefore on the reliability of the parameter estimates.

To determine the challenges met by, and needs of, the epidemiology emergency response workforce, with the aim of informing the development of a larger survey, by conducting key informant interviews of public health experts. We defined our study population as public health experts with experience of epidemiology deployment. Using purposive sampling techniques, we applied random number sampling to shortlists of potential interviewees provided by key organizations to obtain 10 study participants; we identified three additional interviewees through snowballing. The same interviewer conducted all key informant interviews during May-August 2019. We thematically analysed de-identified transcripts using a qualitative data analysis computer software package. Despite our interviewees having a wide range of organizational and field experience, common themes emerged. Interviewees reported a lack of clarity in the definition of an emergency response epidemiologist; the need for a broader range of skills; and inadequate leadership and mentoring in the field. Interviewees identified the lack of interpersonal skills (e.g. communication) and a lack of career progression options as limitations to the effectiveness of emergency response. The epidemiology emergency response workforce is currently not achieving collective competence. The lack of a clear definition of the role must be addressed, and leadership is required to develop teams in which complementary skills are harmonized and those less experienced can be mentored. Epidemiology bodies must consider individual professional accreditation to ensure that the required skills are being achieved, as well as enabling continual professional development.