Simulation Modeling of the Accuracy of Solid-State Wave Gyroscope Characterization with Tuning of Computational Algorithms to Signal Periodicity

The setting of computational algorithms for four methods of identification of wave characteristics in the free run-out mode of standing waves in the resonator of the integrative solid wave gyroscope on observations of signals of its measuring device at time intervals that are multiples of resonator oscillation is described. In the first roughest method, measurement results are processed without taking into account the influence of the quadrature wave. It is convenient for forming initial approximations in the tasks of clarifying optimization of identification functionality in other methods. In the second method, the refined processing of measurement results is carried out taking into account the phase shift of the signals of the measuring device. In the third method, in order to provide better physical visibility and to process the results of measurements, a virtual transition to mobile axes of standing waves is introduced. In the fourth method, measurement results are processed using numerical digital demodulation procedures. Comparisons have been made for the accuracy of these identification techniques by simulation methods for theoretically set source signals. This made it possible to directly compare the original and identified characteristics of wave processes: the amplitude of the main and square standing waves, the angle of the main standing wave and its frequency. The results are presented in the absence and presence of noise in the measuring signals. The results show the specifics of the practical application of different techniques for real samples of gyroscopes. For short identification intervals, they require a special choice of interval lengths equal to the final number of resonator oscillations. With the lengthening of such intervals, this requirement for the length of the interval is weakened.

Jackknifing for partially linear varying-coefficient errors-in-variables model with missing response at random

In this paper, we focus on the response mean of the partially linear varying-coefficient errors-in-variables model with missing response at random. A simulation study is conducted to compare jackknife empirical likelihood method with normal approximation method in terms of coverage probabilities and average interval lengths, and a comparison of the proposed estimators is done based on their biases and mean square errors.

Estimation of a Finite Population Mean under Random Nonresponse Using Kernel Weights

Nonresponse is a potential source of errors in sample surveys. It introduces bias and large variance in the estimation of finite population parameters. Regression models have been recognized as one of the techniques of reducing bias and variance due to random nonresponse using auxiliary data. In this study, it is assumed that random nonresponse occurs in the survey variable in the second stage of cluster sampling, assuming full auxiliary information is available throughout. Auxiliary information is used at the estimation stage via a regression model to address the problem of random nonresponse. In particular, auxiliary information is used via an improved Nadaraya–Watson kernel regression technique to compensate for random nonresponse. The asymptotic bias and mean squared error of the estimator proposed are derived. Besides, a simulation study conducted indicates that the proposed estimator has smaller values of the bias and smaller mean squared error values compared to existing estimators of a finite population mean. The proposed estimator is also shown to have tighter confidence interval lengths at 95% coverage rate. The results obtained in this study are useful for instance in choosing efficient estimators of a finite population mean in demographic sample surveys.

U-Net Segmented Adjacent Angle Detection (USAAD) for Automatic Analysis of Corneal Nerve Structures

Measurement of corneal nerve tortuosity is associated with dry eye disease, diabetic retinopathy, and a range of other conditions. However, clinicians measure tortuosity on very different grading scales that are inherently subjective. Using in vivo confocal microscopy, 253 images of corneal nerves were captured and manually labelled by two researchers with tortuosity measurements ranging on a scale from 0.1 to 1.0. Tortuosity was estimated computationally by extracting a binarised nerve structure utilising a previously published method. A novel U-Net segmented adjacent angle detection (USAAD) method was developed by training a U-Net with a series of back feeding processed images and nerve structure vectorizations. Angles between all vectors and segments were measured and used for training and predicting tortuosity measured by human labelling. Despite the disagreement among clinicians on tortuosity labelling measures, the optimised grading measurement was significantly correlated with our USAAD angle measurements. We identified the nerve interval lengths that optimised the correlation of tortuosity estimates with human grading. We also show the merit of our proposed method with respect to other baseline methods that provide a single estimate of tortuosity. The real benefit of USAAD in future will be to provide comprehensive structural information about variations in nerve orientation for potential use as a clinical measure of the presence of disease and its progression.

Linguistic and individual factors in meaning comprehension

Sentences like “The athlete jumped for 20 minutes / jogged for 2 years.” engender an iterative meaning that is well understood yet morpho-syntactically unsupported—a case manifesting “meaning underspecification.” We argue that such underspecified meaning results from a costly meaning search in the sentential context by evaluating (a) the conventional event duration denoted by the verb, (b) the interval length denoted by the for-adverbial, and (c) the discourse context if there is one. In a naturalness-rating questionnaire and a self- paced reading experiment, we crossed two verb types (Punctual: jump / Durative: jog) and the interval-lengths denoted by for-adverbials (Short: for 20 minutes / Long: for 2 years). In addition, we investigated the impact of individuals’ cognitive styles indexed by autistic tendency on real-time meaning computation. Results showed that, in the absence of discourse context, (i) the harder a specific meaning could be determined from sentential context, the less natural the sentence was perceived, (ii) all sentences with underspecified meaning engendered significantly longer reading times regardless of verb type. Moreover, (iii) individuals with higher autistic tendency lingered significantly longer at the sentence-final region, likely reflecting a difficulty in information integration or a tendency towards deliberative reasoning. These findings suggest that meaning comprehension is lexically-guided and context-constraining, while the online processing profile vary with individuals’ autistic tendency.

Influence function-based empirical likelihood for inference of quantile medical costs with censored data

In this paper, we propose empirical likelihood methods based on influence function and Jackknife techniques to construct confidence intervals for quantile medical costs with censored data. We show that the influence function-based empirical log-likelihood ratio statistic for the quantile medical cost has a standard Chi-square distribution as its asymptotic distribution. Simulation studies are conducted to compare coverage probabilities and interval lengths of the proposed empirical likelihood confidence intervals with the existing normal approximation-based confidence intervals for quantile medical costs. The proposed methods are observed to have better finite-sample performances than existing methods. The new methods are also illustrated through a real example.

Heritability of advertisement call properties of the Japanese quail

The objective of this study was to investigate genetic variances and covariances among features of the male Japanese quail advertisement call. Duration of the first, second and third syllable, the length of interval 1 (between the first and the second syllable), interval 2 (between the second and the third syllable) and damping (extension of the third syllable) were measured as temporal properties of the call. Spectral properties were peak frequencies of each syllable and the damping component. In this study, 1730 calls were recorded from 488 male Japanese quail. The restricted maximum likelihood procedure for repeated measurements was applied to estimate (co)variance components and genetic parameters for the examined traits. Heritability estimates of call parameters of the male Japanese quail ranged from low to high values (0.04-0.65) and they were generally higher for temporal properties than for spectral properties. Among the temporal properties of the call, the highest genetic correlation was between the first and the second syllable (0.96±0.251) while the lowest genetic correlation was between the first and the third syllable (0.03±0.231). Significant genetic correlations were generally high and positive among peak frequencies of the syllables. Despite the lack of apparent pattern, interval lengths tended to have positive correlation with spectral properties of the call, but the correlation of syllable lengths with spectral properties of the call was negative.

Effect of Different Interval Lengths in a Rolling Horizon MILP Unit Commitment with Non-Linear Control Model for a Small Energy System

In this paper, a fixed electricity producer park of both a short- and long-term renewable energy storage (e.g., battery, power to gas to power) and a conventional power plant is combined with an increasing amount of installed volatile renewable power. For the sake of simplicity, the grid is designed as a single copper plate with island restrictions and constant demand of 1000 MW; the volatile input is deducted from scaled 15-min input data of German grid operators. A mixed integer linear programming model is implemented to generate an optimised unit commitment (UCO) for various scenarios and configurations using CPLEX® as the problem solver. The resulting unit commitment is input into a non-linear control model (NLC), which tries to match the plan of the UCO as closely as possible. Using the approach of a rolling horizon the result of the NLC is fed back to the interval of the next optimisation run. The problem’s objective is set to minimise CO2 emissions of the whole electricity producer park. Different interval lengths are tested with perfect foresight. The results gained with different interval lengths are compared to each other and to a simple heuristic approach. As non-linear control model a characteristic line model is used. The results show that the influence of the interval length is rather small, which leads to the conclusion that realistic forecast lengths of two days can be used to achieve not only a sufficient quality of solutions, but shorter computational times as well.