An efficient approach for the construction of LFT S-boxes using chaotic logistic map

In this paper, we synthesize substitution boxes by the use of chaotic logistic maps in linear fractional transformation. In order to introduce randomness in the construction of S-boxes, the data from the chaotic system is used in linear fractional transformation to add additional unpredictable behavior. The proposed S-box is tested for its strength in encryption applications. The nonlinearity characteristic of the proposed S-box is studied, and the strength of the cipher is quantized in terms of this property. In addition, the behavior of bit changes at the output of the cipher in comparison with the input is also studied. Similarly, the input/output differential is also evaluated for different bit patterns. The results of statistical analyses show superior performance of the proposed S-boxes.     

Squeezed flow and heat transfer over a porous surface for viscous fluid

Flow and heat transfer over a permeable sensor surface placed in a squeezing channel is analyzed. A constant transpiration through the sensor surface is assumed. Locally non-similar momentum and energy equations are solved by three different methods, against the transpiration parameter τ, for different values of the squeezing parameter b, and Prandtl number Pr. From the investigation, it is found that when the channel being squeezed, the skin-friction reduces but the heat transfer coefficient increases. Increase in the value of the squeezing parameter onsets reverse flow at the sensor surface when fluid is being injected and the affect is enhanced with the increase of injection through the surface. It is further observed that increase of suction of fluid through the sensor thins the thermal and the momentum boundary layer regions, whereas injection of fluid leads to thickening of both the thermal and the momentum boundary layer regions. Heat transfer from the surface of the sensor increases with the increase of the value of Pr for the entire range of surface mass-flux parameter τ. M. A. Hossain is on leave of absence from University of Dhaka.     

Convective Flows on Reactive Surfaces in Porous Media

A model for the convective flow in a fluidsaturated porous medium containing a reactive component is considered. This component undergoes an exothermic reaction (modelled by a first order mechanism) on an impermeable bounding surface, the resulting heat released driving the convective flow. Large Rayleigh number flow near a stagnation point is treated in detail by first considering the steady states. Multiple solution branches and critical points arising from a hysteresis bifurcation are identified. The form that these solution branches take depends on whether or not the effects of reactant consumption are included. An initialvalue problem is then discussed. This shows that both the lower (slow reaction) and upper (fast reaction) solution branches are stable (and the ultimate state of the system). When the parameter values are such that there is no steady state, the solution develops a finitetime singularity, the nature of which is analysed.     

Fuzzy polynomial regression with fuzzy neural networks

Recently, fuzzy linear regression is considered by Mosleh et al. [1]. In this paper, a novel hybrid method based on fuzzy neural network for approximate fuzzy coefficients (parameters) of fuzzy polynomial regression models with fuzzy output and crisp inputs, is presented. Here a neural network is considered as a part of a large field called neural computing or soft computing. Moreover, in order to find the approximate parameters, a simple algorithm from the cost function of the fuzzy neural network is proposed. Finally, we illustrate our approach by some numerical examples.     

Catalyst-free one-pot synthesis of 1,4,5-trisubstituted pyrazoles in 2,2,2-trifluoroethanol

A simple, efficient and three component one-pot synthesis of 1,4,5-trisubstituted pyrazoles by condensation of β-dicarbonyls, N,N-dimethylformamide dimethyl acetal (DMFDMA) and hydrazine derivatives in 2,2,2-trifluoroethanol without using any catalyst and activation, is described.     

Bioresource Polymer Composite for Energy Generation and Storage: Developments and Trends

The ever-growing demand of human society for clean and reliable energy sources spurred a substantial academic interest in exploring the potential of biological resources for developing energy generation and storage systems. As a result, alternative energy sources are needed in populous developing countries to compensate for energy deficits in an environmentally sustainable manner. This review aims to evaluate and summarize the recent progress in bio-based polymer composites (PCs) for energy generation and storage. The articulated review provides an overview of energy storage systems, e. g., supercapacitors and batteries, and discusses the future possibilities of various solar cells (SCs), using both past research progress and possible future developments as a basis for discussion. These studies examine systematic and sequential advances in different generations of SCs. Developing novel PCs that are efficient, stable, and cost-effective is of utmost importance. In addition, the current state of high-performance equipment for each of the technologies is evaluated in detail. We also discuss the prospects, future trends, and opportunities regarding using bioresources for energy generation and storage, as well as the development of low-cost and efficient PCs for SCs.     

DAD-Net: Classification of Alzheimer’s Disease Using ADASYN Oversampling Technique and Optimized Neural Network

Alzheimer’s Disease (AD) is a neurological brain disorder that causes dementia and neurological dysfunction, affecting memory, behavior, and cognition. Deep Learning (DL), a kind of Artificial Intelligence (AI), has paved the way for new AD detection and automation methods. The DL model’s prediction accuracy depends on the dataset’s size. The DL models lose their accuracy when the dataset has an imbalanced class problem. This study aims to use the deep Convolutional Neural Network (CNN) to develop a reliable and efficient method for identifying Alzheimer’s disease using MRI. In this study, we offer a new CNN architecture for diagnosing Alzheimer’s disease with a modest number of parameters, making it perfect for training a smaller dataset. This proposed model correctly separates the early stages of Alzheimer’s disease and displays class activation patterns on the brain as a heat map. The proposed Detection of Alzheimer’s Disease Network (DAD-Net) is developed from scratch to correctly classify the phases of Alzheimer’s disease while reducing parameters and computation costs. The Kaggle MRI image dataset has a severe problem with class imbalance. Therefore, we used a synthetic oversampling technique to distribute the image throughout the classes and avoid the problem. Precision, recall, F1-score, Area Under the Curve (AUC), and loss are all used to compare the proposed DAD-Net against DEMENET and CNN Model. For accuracy, AUC, F1-score, precision, and recall, the DAD-Net achieved the following values for evaluation metrics: 99.22%, 99.91%, 99.19%, 99.30%, and 99.14%, respectively. The presented DAD-Net outperforms other state-of-the-art models in all evaluation metrics, according to the simulation results.     

Trident Nano-Indexing the Proteomics Table: Next-Version Clustering of Iron Carbide NPs and Protein Corona

Protein corona composition and precise physiological understanding of differentially expressed proteins are key for identifying disease biomarkers. In this report, we presented a distinctive quantitative proteomics table of molecular cell signaling differentially expressed proteins of corona that formed on iron carbide nanoparticles (NPs). High-performance liquid chromatography/electrospray ionization coupled with ion trap mass analyzer (HPLC/ESI-Orbitrap) and MASCOT helped quantify 142 differentially expressed proteins. Among these proteins, 104 proteins showed upregulated behavior and 38 proteins were downregulated with respect to the control, whereas 48, 32 and 24 proteins were upregulated and 8, 9 and 21 were downregulated CW (control with unmodified NPs), CY (control with modified NPs) and WY (modified and unmodified NPs), respectively. These proteins were further categorized on behalf of their regularity, locality, molecular functionality and molecular masses using gene ontology (GO). A STRING analysis was used to target the specific range of proteins involved in metabolic pathways and molecular processing in different kinds of binding functionalities, such as RNA, DNA, ATP, ADP, GTP, GDP and calcium ion bindings. Thus, this study will help develop efficient protocols for the identification of latent biomarkers in early disease detection using protein fingerprints.     

Sub-Nominal Resolution Fourier Transform Spectrometry with Chip-Based Combs

Chip-based optical frequency combs address the demand for compact, bright, coherent light sources of equidistant phase-locked lines. Traditionally, the Fourier Transform Spectroscopy (FTS) technique is considered a suboptimal choice for resolving comb lines in chip-based sensing applications due to the requirement of long optical delays, and spectral distortion from the instrumental line shape. Here, a sub-nominal resolution FTS technique is developed that extracts the comb's offset frequency in any spectral region directly from the measured interferogram without resorting to nonlinear $$-to- $$ interferometry. This in turn enables 10's of MHz resolution spectrometry with millimeter optical retardations currently limited by the emission linewidth and phase noise of the used lasers. Low-pressure Doppler-broadened absorption lines probed by widely-tunable chip-scale mid-infrared optical frequen with electrical pumping are fully resolved over a span of tens of nanometers. This versatile technique paves the way for compact, electrostatically-actuated, or even all-on-chip high-fidelity FTS, and can be readily applied to boost the resolution of existing commercial instruments with compact interferometers several hundred times.     

Spectrophotometric determination of tranexamic acid in pharmaceutical bulk and dosage forms.

In this study, a simple, fast, accurate and sensitive spectrophotometric method has been developed for the determination of tranexamic acid in bulk and pharmaceutical preparations. The method is based on the reaction of ninhydrin with the primary amino group of tranexamic acid in the basic medium at pH 8.0. The reaction produces a bluish-purple color which absorbs maximally at 565 nm. Beer's law was obeyed in the range of 3-40 microg ml(-1) with molar absorptivity of 5.093 x 10(3) L mol(-1) cm(-1). The effects of various factors such as temperature, heating time, concentration of reagent, color stability and interferences were investigated to optimize the procedure. The results have been validated analytically and statistically. The proposed method has been applied for the determination of tranexamic acid in bulk and pharmaceutical preparations with good results.