Prediction of Time Series Empowered with a Novel SREKRLS Algorithm

For the unforced dynamical non-linear statespace model, a new Q1 and efficient square root extended kernel recursive least square estimation algorithm is developed in this article. The proposed algorithm lends itself towards the parallel implementation as in the FPGA systems. With the help of an ortho-normal triangularization method, which relies on numerically stable givens rotation, matrix inversion causes a computational burden, is reduced. Matrix computation possesses many excellent numerical properties such as singularity, symmetry, skew symmetry, and triangularity is achieved by using this algorithm. The proposed method is validated for the prediction of stationary and non-stationary MackeyGlass Time Series, along with that a component in the x-direction of the Lorenz Times Series is also predicted to illustrate its usefulness. By the learning curves regarding mean square error (MSE) are witnessed for demonstration with prediction performance of the proposed algorithm from where it’s concluded that the proposed algorithm performs better than EKRLS. This new SREKRLS based design positively offers an innovative era towards non-linear systolic arrays, which is efficient in developing very-large-scale integration (VLSI) applications with non-linear input data. Multiple experiments are carried out to validate the reliability, effectiveness, and applicability of the proposed algorithm and with different noise levels compared to the Extended kernel recursive least-squares (EKRLS) algorithm.     

Oversampling Method Based on Gaussian Distribution and K-Means Clustering

Learning from imbalanced data is one of the greatest challenging problems in binary classification, and this problem has gained more importance in recent years. When the class distribution is imbalanced, classical machine learning algorithms tend to move strongly towards the majority class and disregard the minority. Therefore, the accuracy may be high, but the model cannot recognize data instances in the minority class to classify them, leading to many misclassifications. Different methods have been proposed in the literature to handle the imbalance problem, but most are complicated and tend to simulate unnecessary noise. In this paper, we propose a simple oversampling method based on Multivariate Gaussian distribution and K-means clustering, called GK-Means. The new method aims to avoid generating noise and control imbalances between and within classes. Various experiments have been carried out with six classifiers and four oversampling methods. Experimental results on different imbalanced datasets show that the proposed GK-Means outperforms other oversampling methods and improves classification performance as measured by F1-score and Accuracy.     

Magnetic Polaron States in Photoluminescent Carbon Dots Enable Hydrogen Peroxide Photoproduction (Small 32/2023)

Photoactivation of aspartic acid-based carbon dots (Asp-CDs) induces the generation of spin-separated species, including electron/hole (e⁻/h⁺) polarons and spin-coupled triplet states, as uniquely confirmed by the light-induced electron paramagnetic resonance spectroscopy. The relative population of the e⁻/h⁺ pairs and triplet species depends on the solvent polarity, featuring a substantial stabilization of the triplet state in a non-polar environment (benzene). The electronic properties of the photoexcited Asp-CDs emerge from their spatial organization being interpreted as multi-layer assemblies containing a hydrophobic carbonaceous core and a hydrophilic oxygen and nitrogen functionalized surface. The system properties are dissected theoretically by density functional theory in combination with molecular dynamics simulations on quasi-spherical assemblies of size-variant flakelike model systems, revealing the importance of size dependence and interlayer effects. The formation of the spin-separated states in Asp-CDs enables the photoproduction of hydrogen peroxide (H₂O₂) from water and water/2-propanol mixture via a water oxidation reaction.     

Natural functionalisation of a traditional textile “Ehram”

This study is focused on the natural functionalisation of a traditional woven fabric called “Ehram”. Which is a wool based woven fabric constituted from wool yarns that are originally coloured and used in different cities of the Eastern Anatolian Region, Turkey. Because these fabrics were originally coloured (pigmented), the study aimed to functionalise them with minimal colour changes from their original colour. For this aim; lavender (Lavandula angustifolia) which is also a natural source of pleasant odour was used in finishing of Ehram. The colour changes by the application of the lavender on Ehram and the benefits that come out from the use of lavender were then analysed and presented in the study. In this respect, antibacterial activities (against Staphylococcus aureus and Escherichia coli) of dyed samples were tested within the scope of the study. In addition, colour measurements (K/S and CIE L*a*b* C* and h^o) and FTIR (Fourier-transform infrared) microspectroscopy analyses of dyed fabric samples were made and microscope images were taken. It was observed that lavender with a copper-based mordanting agent can present antibacterial activity against S. aureus and E. coli in Ehram.     

Topping of naturally dyed wool fabrics with different natural dye sources

Wool fabrics can be dyed with synthetic dyes and even with natural dyes. To present a different aspect to the coloration of wool, the current study was focused on a topping process (ie, the dyeing of wool that was already dyed) with different natural dye sources. For this purpose, the fabrics that were already dyed with a natural dye source were once again dyed with two different natural dyes. In bottom dyeing (bottoming), fabrics were dyed with hops (Humulus lupulus L.) in the presence of different mordanting agents. Then the dyed, washed and dried samples were once again dyed (topping) with two different natural dye sources. For topping, powdered madder and acorn were tested in direct dyeing of wool samples. Finally, the colour changes were analysed with the use of a spectrophotometer. The study demonstrates that such a process (ie, bottoming with hops in the presence of different mordanting agents and then topping with madder or acorn) can be a way of obtaining different shades and colours with sufficient/good fastness values by natural dyeing.     

Scattering of Laguerre–Gaussian beam from a chiral-coated perfect electromagnetic conductor (PEMC) cylinder

Journal of Computational Electronics - A theoretical analysis of Laguerre–Gaussian (LG) beam scattering from a chiral-coated perfect electromagnetic conductor (PEMC) cylinder is presented....     

Improving Brain Tumor Classification with Deep Learning UsingSyntheticData

Deep learning (DL) techniques, which do not need complex pre-processing and feature analysis, are used in many areas of medicine and achieve promising results. On the other hand, in medical studies, a limited dataset decreases the abstraction ability of the DL model. In this context, we aimed to produce synthetic brain images including three tumor types (glioma, meningioma, and pituitary), unlike traditional data augmentation methods, and classify them with DL. This study proposes a tumor classification model consisting of a Dense Convolutional Network (DenseNet121)-based DL model to prevent forgetting problems in deep networks and delay information flow between layers. By comparing models trained on two different datasets, we demonstrated the effect of synthetic images generated by Cycle Generative Adversarial Network (CycleGAN) on the generalization of DL. One model is trained only on the original dataset, while the other is trained on the combined dataset of synthetic and original images. Synthetic data generated by CycleGAN improved the best accuracy values for glioma, meningioma, and pituitary tumor classes from 0.9633, 0.9569, and 0.9904 to 0.9968, 0.9920, and 0.9952, respectively. The developed model using synthetic data obtained a higher accuracy value than the related studies in the literature. Additionally, except for pixel-level and affine transform data augmentation, synthetic data has been generated in the figshare brain dataset for the first time.     

Impact of salicylic acid and calcium chloride on quality attributes of peach stored at refrigeration temperature

Food Science and Biotechnology - Salicylic acid (SA) in different concentration were used to assess its individual effect as well as combined impact with 3% calcium chloride (CaCl2) on Swat No. 8...