Short-term and long-term memory analysis of learning using 2D and 3D educational contents

ABSTRACT

The effect of 2D and 3D educational content learning on memory has been studied using electroencephalography (EEG) brain signal. A hypothesis is set that the 3D materials are better than the 2D materials for learning and memory recall. To test the hypothesis, we proposed a classification system that will predict true or false recall for short-term memory (STM) and long-term memory (LTM) after learning by either 2D or 3D educational contents. For this purpose, EEG brain signals are recorded during learning and testing; the signals are then analysed in the time domain using different types of features in various frequency bands. The features are then fed into a support vector machine (SVM)-based classifier. The experimental results indicate that the learning and memory recall using 2D and 3D contents do not have significant differences for both the STM and the LTM.     

A nonlinear quantization scheme for two-layer HDR image encoding

Two-layer schemes provide an effective method of encoding high dynamic range images with backward compatibility. The first layer is the tone-mapped low dynamic range version of the original image, used for visualization. The residual information that cannot be preserved in the first layer is stored in the second layer, which itself is generally encoded as an image of a fixed bit-depth. Any further details that cannot be preserved in the second layer are discarded. In this paper, we present a nonlinear quantization algorithm that can significantly enhance the amount of details that can be preserved in the second layer, and therefore improve the encoding efficiency. The proposed technique can be incorporated in any existing two-layer encoding method and leads to significant improvement in their performance.     

Modeling of Dispersive Materials Using Dispersion Models for FDTD Application

A nonlinear optimization algorithm based on the Nelder Mead method is used to characterize the frequency dependent permittivity of 15 materials based on Lorentz, modified Lorentz, Drude and Lorentz-Drude models. The optimized model parameters are used to calculate the complex relative permittivity of each material and compare it with experimental data. In each case, a very good match is found between the optimized and experimental data over a long wavelength range. Comparative study of the models used for each material is performed based on accuracy, wavelength range of applicability and computational efficiency. The parameters presented can be used for computer simulation of electromagnetic wave phenomena involving these materials.     

Hybrid model of content extraction

We present a hybrid model for content extraction from HTML documents. The model operates on Document Object Model (DOM) tree of the corresponding HTML document. It evaluates each tree node and associated statistical features like link density and text distribution across the node to predict significance of the node towards overall content provided by the document. Once significance of the nodes is determined, the formatting characteristics like fonts, styles and the position of the nodes are evaluated to identify the nodes with similar formatting as compared to the significant nodes. The proposed hybrid model is derived from two different models, i.e., one is based on statistical features and other on formatting characteristics and achieved the best accuracy. We describe the validity of model with the help of experiments conducted on the standard data sets. The results revealed that the proposed model outperformed other existing content extraction models. We present a browser based implementation of the proposed model as proof of concept and compare the implementation strategy with various state of art implementations. We also discuss various applications of the proposed model with special emphasis on open source intelligence.     

Rice-like CuO nanostructures for sensitive electrochemical sensing of hydrazine

In this work, a low temperature aqueous chemical growth methodology was used for the fabrication of CuO nanostructures. The as-synthesised nanostructures were then elaborately characterised by number of analytical techniques such as scanning electron microscopy (SEM) and X-ray powder diffraction (XRD). The obtained nanostructures were observed to possess interlaced rice-shaped structural features with the length and width of individual rice determined to be in the range of 200–300 nm and 50–100 nm respectively. The unique nanostructures when utilised as electrode material exhibited excellent electro-catalytic potential towards oxidation of hydrazine in alkaline media. The excellent conductive of CuO added by the high surface area of obtained nanorice-like structures enabled development of highly sensitive (3087 µA mM⁻¹ cm⁻²), selective and stable electrochemical sensor for hydrazine. In addition, the successfully application of the developed sensor in spiked tap, bottled and industrial water samples for the detection of hydrazine suggested its feasibility for practical environmental application.

     

Distributed composition of complex event services in IoT network

The Journal of Supercomputing - In this paper, a distributed solution is presented for the composition of complex event services in the Internet of Things (IoT) environments. The composition of...     

The Investigation of the Fractional-View Dynamics of Helmholtz Equations Within Caputo Operator

It is eminent that partial differential equations are extensively meaningful in physics, mathematics and engineering. Natural phenomena are formulated with partial differential equations and are solved analytically or numerically to interrogate the system’s dynamical behavior. In the present research, mathematical modeling is extended and the modeling solutions Helmholtz equations are discussed in the fractional view of derivatives. First, the Helmholtz equations are presented in Caputo’s fractional derivative. Then Natural transformation, along with the decomposition method, is used to attain the series form solutions of the suggested problems. For justification of the proposed technique, it is applied to several numerical examples. The graphical representation of the solutions shows that the suggested technique is an accurate and effective technique with a high convergence rate than other methods. The less calculation and higher rate of convergence have confirmed the present technique’s reliability and applicability to solve partial differential equations and their systems in a fractional framework.     

Theranostic Interpolation of Genomic Instability in Breast Cancer

Breast cancer is a diverse disease caused by mutations in multiple genes accompanying epigenetic aberrations of hazardous genes and protein pathways, which distress tumor-suppressor genes and the expression of oncogenes. Alteration in any of the several physiological mechanisms such as cell cycle checkpoints, DNA repair machinery, mitotic checkpoints, and telomere maintenance results in genomic instability. Theranostic has the potential to foretell and estimate therapy response, contributing a valuable opportunity to modify the ongoing treatments and has developed new treatment strategies in a personalized manner. “Omics” technologies play a key role while studying genomic instability in breast cancer, and broadly include various aspects of proteomics, genomics, metabolomics, and tumor grading. Certain computational techniques have been designed to facilitate the early diagnosis of cancer and predict disease-specific therapies, which can produce many effective results. Several diverse tools are used to investigate genomic instability and underlying mechanisms. The current review aimed to explore the genomic landscape, tumor heterogeneity, and possible mechanisms of genomic instability involved in initiating breast cancer. We also discuss the implications of computational biology regarding mutational and pathway analyses, identification of prognostic markers, and the development of strategies for precision medicine. We also review different technologies required for the investigation of genomic instability in breast cancer cells, including recent therapeutic and preventive advances in breast cancer.     

Development of High Strength and Toughness Non-Heated Al–Mg–Si Alloys for High-Pressure Die-Casting

Based on the 3 factors and 3 levels orthogonal experiment method, compositional effects of Mg, Si, and Ti addition on the microstructures, tensile properties, and fracture behaviors of the high-pressure die-casting Al-x Mg-y Si-z Ti alloys have been investigated. The analysis of variance shows that both Mg and Si apparently infl uence the tensile properties of the alloys, while Ti does not. The tensile mechanical properties are comprehensively infl uenced by the amount of eutectic phase(α-Al + Mg_2Si), the average grain size, and the content of Mg dissolved into α-Al matrix. The optimized alloy is Al-7.49 Mg-3.08 Si-0.01 Ti(wt%), which exhibits tensile yield strength of 219 MPa, ultimate tensile strength of 401 MPa, and elongation of 10.5%. Furthermore, contour maps, showing the relationship among compositions, microstructure characteristics, and the tensile properties are constructed, which provide guidelines for developing high strength and toughness Al–Mg–Si–Ti alloys for high-pressure die-casting.