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The globe is experiencing freshwater scarcity resulting from untreated effluents entering freshwater streams. Developing cost-effective materials to treat industrial effluents before entering freshwater is crucial. Herein, ${\mathrm{\text{K}}}_2{\mathrm{\text{BaCoO}}}_3$ (KBCO) perovskite nanomaterials of naturally abundant, nontoxic, eco-friendly elements were synthesized through a facile hydrothermal synthesis route. Various characterization techniques were used to investigate the synthesis of the resulting material. The SEM images of the synthesized catalyst reveal irregular morphology and rough surfaces. FTIR and EDX analyses confirmed the successful synthesis of KBCO while TEM analysis revealed a polycrystalline nature with an average crystallite size of 13nm. At optimum conditions (50min sunlight irradiation, 0.015g KBCO, 40mg ${\mathrm{\text{L}}}^{-1}$ MB dye and pH 11), 99.08% MB dye degradation was observed. The reaction kinetics fits the first-order kinetic model with ${\mathrm{\text{R}}}^2$ values of 0.99. The photocatalysis was statistically optimized/validated by analysis of variance (ANOVA) Box–Behnken design of the response surface methodology (RSM). The predicted ${\mathrm{\text{R}}}^2$ of 0.84 is in reasonable agreement with the adjusted ${\mathrm{\text{R}}}^2$ of 0.99; adequate precision of 347.86 indicates an adequate signal which shows that the model can be used to navigate the design space. The model $F$ value is 15414.00, while the $p$-value of more than ten terms is less than 0.05, indicating the significance of the model. A standard deviation of 0.32, and no significant lack of fit of 2.41 confirms it as the best-fitted model.

In recent years, artificial neural networks and their applications for large data sets have become a crucial part of scientific research. In this work, we implement the Multilayer Perceptron (MLP), which is a class of feedforward artificial neural network (ANN), to predict ground-state binding energies of atomic nuclei. Two different MLP architectures with three and four hidden layers are used to study their effects on the predictions. To train the MLP architectures, two different inputs are used along with the latest atomic mass table and changes in binding energy predictions are also analyzed in terms of the changes in the input channel. It is seen that using appropriate MLP architectures and putting more physical information in the input channels, MLP can make fast and reliable predictions for binding energies of atomic nuclei, which is also comparable to the microscopic energy density functionals.

The paper discusses the existing theories and measurement tools of self-esteem and self-liking and analyzes certain non-clinical ways for improving them. Statistical modeling is used to confirm the effect of receiving compliments on self-liking. The experimental part of the paper is based on a questionnaire about self-perceived attractiveness completed by a group of young adults, half of whom received a compliment immediately before. The modeling outcomes demonstrate that compliments lead to increased self-perceived attractiveness in both genders.

Statistical problems encountered in the study of the influence of random inhomogeneities in layered shallow water on the propagation of sound signal is considered. The study is carried out by the example of two-layer models of the sea — a stochastic Pekeris waveguide and a waveguide with a regular refraction in the water layer, which describes the presence of the thermocline. The results were obtained by statistical simulation without approximations and assumptions. In the middle frequency range for actual parameters of sound speed fluctuations in shallow sea with a loss penetrable bottom, the specific features of acoustic field statistical moments behavior have been discovered. They did not get adequate attention in the scientific literature.

Purpose. The primary objective of the study was to determine a way to obtain subject-specific body dimensions. The aim was then to develop linear statistical models for estimating human body geometry from a small number of body measurements. Methods. Internal (bone dimensions) and external (body) measurements were collected on 64 healthy adults representative of three morphotypes. Simple and multiple linear regression models between external and internal body dimensions were then obtained and assessed. Results. The statistical analysis provided 184 anthropometrical models which allow estimation of subject-specific external and internal data from 10 external dimensions that can be easily measured on any subject. Among them, 62 models had a 2SEE (i.e. twice the Standard Error of Estimate) lower than 10%. Conclusion. This study proposes a non-invasive approach to estimate both external and internal body dimensions.

Semantic segmentation is a pre-processing step in computer vision-based applications. It is the task of assigning a predefined class label to every pixel of an image. Several supervised and unsupervised algorithms are available to classify pixels of an image into predefined object classes. The algorithms, such as random forest and SVM are used to obtain the semantic segmentation. Recently, convolutional neural network (CNN)-based architectures have become popular for the tasks of object detection, object recognition, and segmentation. These deep architectures perform semantic segmentation with far better accuracy than the algorithms that were used earlier. CNN-based deep learning architectures require a large dataset for training. In real life, some of the applications may not have sufficient good quality samples for training of deep learning architectures e.g. medical applications. Such a requirement initiated a need to have a technique of effective training of deep learning architecture in case of a very small dataset. Class imbalance is another challenge in the process of training deep learning architecture. Due to class imbalance, the classifier overclassifies classes with large samples. In this paper, the challenge of training a deep learning architecture with a small dataset and class imbalance is addressed by novel fusion-based semantic segmentation technique which improves segmentation of minor and major classes.

The author introduces a statistical model of literary style that allows for the authorship question to be examined. This model uses Markov modeling to examine underlying patterns in pieces of literature. Different methods for comparing these models are examined. The methods are then applied to an experiment involving the authorship of 16th century sonnets. Finally suggestions for improving on this technique are discussed.

The Analytic Hierarchy Process (AHP) uses pairwise comparison to evaluate alternatives' advantages to a certain criterion. For decision-making problem with many different criteria and alternatives, pairwise comparison causes a prolonged decision-making period and rises fatigue in decision-makers' mentality. A question of practical value is if there exists a way to reduce judgment number and what influence the reduction will have on the overall evaluation of alternative ratings. To answer this question, we introduce scale error and judgment error into AHP judgment matrix. By expanding the scales defined in the AHP, scale error is eliminated. Taking judgment error as random variable, a new estimator to calculate priority vector is presented. In the end, an example is proved to show lowering judgment number will increase the probability of larger errors appearing in priority vector computation.

Statistical modeling of wavelet coefficients is a critical issue in wavelet domain signal processing. By analyzing the defects of other existing methods, and exploiting the local dependency of wavelet coefficients, an efficient statistical model is proposed. Improved variance estimation of the local wavelet coefficients can be obtained using the new model. Then we apply an approximate minimum mean squared error (MMSE) estimation procedure to restore the wavelet image coefficients. The modeling process is computational cost saving, and the denoising experiments show the algorithm outperforms other approaches in peak-signal-to-noise ratio (PSNR).

Removing the ambient noise and increasing the signal-to-noise ratio are very important for detecting defects and corrosions of conductive material by using the electromagnetic acoustic transducer. It is still an issue to remove the ambient noise without losing the original signal information. The aim of this paper is to solve the issue by using a new closed-form shrinkage function based on Gauss–Laplace mixture distribution in wavelet domain. First, we prove that Gauss–Laplace mixture distribution is well fitted to the statistical model for wavelet coefficients of noise-free signal of electromagnetic acoustic transducer. As well, we use Gauss–Laplace mixture distribution and Gauss distribution for statistical modeling on the wavelet coefficients of noise-free signal and ambient noise, respectively. Using these distributions, we derive a new closed-form shrinkage function that is an analytical solution of a Bayesian maximum a posteriori estimator. Next, we evaluate the denoising performance of new shrinkage function compared with various shrinkage functions in terms of the improved signal-to-noise ratio, root mean squared error and entropy. The experiment results show that the wavelet denoising method using the proposed shrinkage function effectively removes the ambient noise than the other existing denoising methods for noisy signal of electromagnetic acoustic transducer.

Dropping the ambient noise from microseismic signals is very important for disaster monitoring such as a rockburst and early warning system using microseismic monitoring techniques in the mine and coal mines. Currently, it is still a challenge to remove high and low-frequency noise simultaneously without losing the useful information of microseismic signal. The aim of this paper is to remove the low-frequency noise contained in microseismic signal effectively, while preserving the useful signal information by using a stochastic approach. We first statistically model the wavelet coefficients in the approximation subband of noisy microseismic signal. In addition, we evaluate qualitatively and quantitatively the fitness of Gauss–Laplace mixture distribution and the statistical modeling of data. Then, we propose a novel denoising algorithm to remove the ambient noise effectively from the noisy microseismic signals in wavelet domain. This algorithm removes the low-frequency noise by using a stochastic approach and the high-frequency noise by using a traditional wavelet thresholding method. The low-frequency noise is removed by using a closed-form shrinkage function based on Gauss–Laplace mixture distribution, while the high-frequency noise is removed by using a threshold function combined with Garrote and hyperbolic threshold functions. Next, we evaluated the ambient denoising performance of our novel denoising algorithm by comparing it with various denoising methods with different test signals. Experimental results show that the ambient denoising performance of the proposed method is better than the other seven existing methods.

The ability to predict coastline evolution in the long term is of great importance to coastal managers for social, economic and environmental risk assessment purposes. A Bayesian statistical approach to the problem of modeling and predicting the occurrence of major failure events in soft coastal cliffs is presented. This approach combines available data with expert judgement about the behavior of the cliff under study. Expert judgement is described in the form of expected values for cliff failure occurrences, confidence or degree of belief and subjective probability of occurrence of extremes. WinBUGS software is used to derive predictive probabilities, estimates of the rate of failure and standard deviations, which combine all information. The statistical method is illustrated in the cases of cliffs situated in Herne Bay and Scarborough, UK. The results can be used in a risk-based assessment of coastal cliff recession.

This chapter describes a methodology for the construction of 3D atlases of the heart suitable for the generation of statistical shape models from large image databases of dynamic cardiac MRI and SPECT studies. The presented approach makes use of non-rigid registration for the automatic establishing of landmark correspondences across a training set of shapes. The main properties of the constructed shape models are presented, and their potential in terms of the ultimate goal of image segmentation is also discussed.