Numerical study of highly dispersive optical solitons with differential group delay having quadratic-cubic law of refractive index by Laplace–Adomian decomposition

This paper carries out numerical simulations of highly dispersive optical solitons with differential group delay having quadratic-cubic law of nonlinearity. The Laplace–Adomian decomposition scheme is implemented to visualize the soliton propagation dynamics. Both bright and dark solitons are addressed. The error measure for these numerical approximations is impressively low as presented.

Neutron optical test of completeness of quantum root-mean-square errors

While in classical mechanics the mean error of a measurement is solely caused by the measuring process (or device), in quantum mechanics the operator-based nature of quantum measurements has to be considered in the error measure as well. One of the major problems in quantum physics has been to generalize the classical root-mean-square error to quantum measurements to obtain an error measure satisfying both soundness (to vanish for any accurate measurements) and completeness (to vanish only for accurate measurements). A noise-operator-based error measure has been commonly used for this purpose, but it has turned out incomplete. Recently, Ozawa proposed an improved definition for a noise-operator-based error measure to be both sound and complete. Here, we present a neutron optical demonstration for the completeness of the improved error measure for both projective (or sharp) as well as generalized (or unsharp) measurements.

Fuzzy Time Series for Projecting School Enrolment in Malaysia

There are a variety of approaches to the problem of predicting educational enrolment. However, none of them can be used when the historical data are linguistic values. Fuzzy time series is an efficient and effective tool to deal with such problems. In this paper, the forecast of the enrolment of pre-primary, primary, secondary, and tertiary schools in Malaysia is carried out using fuzzy time series approaches. A fuzzy time series model is developed using historical dataset collected from the United Nations Educational, Scientific, and Cultural Organization (UNESCO) from the year 1981 to 2018. A complete procedure is proposed which includes: fuzzifying the historical dataset, developing a fuzzy time series model, and calculating and interpreting the outputs. The accuracy of the model are also examined to evaluate how good the developed forecasting model is. It is tested based on the value of the mean squared error (MSE), Mean Absolute Percent Error (MAPE) and Mean Absolute Deviation (MAD). The lower the value of error measure, the higher the accuracy of the model. The result shows that fuzzy time series model developed for primary school enrollments is the most accurate with the lowest error measure, with the MSE value being 0.38, MAPE 0.43 and MAD 0.43 respectively.

A Hybrid Wavelet-Arima Model for Standardized Precipitation Index Drought Forecasting

ABSTRACTThis paper proposes A Hybrid Wavelet-Auto-Regressive Integrated MovingAverage (W-ARIMA) model to explore the ability of the hybrid model over an ARIMAmodel. It combines two methods, a Discrete Wavelet Transform (DWT) and ARIMAmodel using the Standardized Precipitation Index (SPI) drought data for forecastingdrought modeling development. SPI data from January 1954 to December 2008 used wasdivided into two - (80%/20% for training/testing respectively). The results were comparedwith the conventional ARIMA model with Mean Square Error (MSE) and Mean AverageError (MAE) as an error measure. The results of the proposed method achieved the bestforecasting performance.

The Exact Query Complexity of Yes-No Permutation Mastermind

Mastermind is famous two-player game. The first player (codemaker) chooses a secret code which the second player (codebreaker) is supposed to crack within a minimum number of code guesses (queries). Therefore, the codemaker’s duty is to help the codebreaker by providing a well-defined error measure between the secret code and the guessed code after each query. We consider a variant, called Yes-No AB-Mastermind, where both secret code and queries must be repetition-free and the provided information by the codemaker only indicates if a query contains any correct position at all. For this Mastermind version with n positions and k ≥ n colors and ℓ : = k + 1 − n , we prove a lower bound of ∑ j = ℓ k log 2 j and an upper bound of n log 2 n + k on the number of queries necessary to break the secret code. For the important case k = n , where both secret code and queries represent permutations, our results imply an exact asymptotic complexity of Θ ( n log n ) queries.

Predicting fatalities among shark attacks: comparison of classifiers

<span lang="EN-US">This paper presents the comparisons of different classifiers on predicting Shark attack fatalities. In this study, we are comparing two classifiers which are Support vector machines(SVMs) and Bayes Point Machines(BPMs) on Shark attacks dataset. The comparison of the classifiers were based on the accuracy, recall, precision and F1-score as the performance measurement. The results obtained from this study showed that BPMs predicted the fatality of shack attack victim experiment with higher accuracy and precision than the SVMs because BPMs have “average” identifier which can minimize the probabilistic error measure. From this experiment, it is concluded that BPMs are more suitable in predicting fatality of shark attack victim as BPMs is an improvement of SVMs.</span>