Polyphenols and Bionanoparticle Combinations as Neuroprotective and Anticarcinogenic Agents: A Potential Neuropharmacological Solution to Combat Cerebral Stroke and Cancer

Polyphenols are naturally occurring organic materials that can be leveraged to develop extremely efficient drug agents for prevention in cerebral stroke related injuries and for chemotherapeutic purposes. Ongoing research on clinically important polyphenolic materials such as resveratrol and flavanols has indicated an immense potential for new drug design for treatment and prevention of cerebral ischemic stroke and other novel chemotherapeutic applications. In this short review, we have highlighted recent developments in this area to facilitate further research on this important class of organic materials for new biomedical applications that include nanoparticles based drug delivery systems.     

A comparison of the performance analysis of the spark ignition engine fuelled with ethanol–iso-octane and methanol–iso-octane fuel blends

In this research, the effects of unleaded iso-octane (base fuel), iso-octane–ethanol blend (E20) and iso-octane–methanol blend (M20) on engine performance were investigated experimentally in a single-cylinder four-stroke spark-ignition engine. The tests were performed by varying the throttle position and engine speed at a constant load of 8 kg. The engine speed was varied from 1200 to 1750 rpm, with changing the throttle position. The results showed that ethanol and methanol addition to unleaded iso-octane increases the engine torque, power and brake-specific fuel consumption (BSFC) in comparison to base fuel. The results also showed that exhaust temperature increases with the increase in engine speed. The thermal efficiency varies from 14.3% to 35.9% for iso-octane, 20.1–30.59% for E20 and (17.64–27.46%) for M20 fuel. It was also found that the volumetric efficiency of M20 and E20 fuels was higher than that of iso-octane in all speed ranges.     

Determination of benzimidazoles in pharmaceuticals and human serum by high-performance liquid chromatography

A liquid chromatographic method is described for the quantification of prednisolone, benzimidazoles, and preservatives using a C₁₈ analytical column as stationary phase. The mobile phase was 30:70 methanol:pH 2.5 phosphate buffer at a flow rate of 1.0 mL min^?1 with absorbance detection at 235 nm. The method was linear for concentrations ranged from 40–10,000 ng mL^?1. Low values of coefficient of variance were obtained when samples were analyzed as replicates. Excellent recovery values were recorded in commercial products and fortified samples. International Conference of Harmonization protocols were employed to perform comprehensive method validation. The reported method has applications for pharmaceutical and serum samples.     

Development and characterization of jute/polypropylene composite by using comingled nonwoven structures

High melt viscosity of thermoplastic matrices hinders the impregnation of fibers and their consolidation during fabrication of composites. In the present study, matrix (polypropylene) and reinforcement (jute) fibers were comingled at fiber to fiber level (fiber to matrix ratio of 30, 40, 50 and 60%) while making their nonwoven fabrics using dry laid process. Thermoforming process, in which polypropylene fibers are used and then melted for the development of composite plates. The results showed that the composite samples with 0.30 fiber volume fraction have higher mechanical properties (flexural, impact energy), while samples with 0.40 fiber volume fraction have higher tensile properties (tensile strength and modulus) due to good impregnation of fiber and better fiber-matrix interface. The composite having 0.30 fiber volume fraction has lowest value of moisture regain and diffusion coefficients than the other samples. Voids present in the microscopic images also confirmed the weak interface, when the fiber volume fraction increased up to 0.60.     

The development of novel auxetic woven structure for impact applications

Nowadays, the auxetic materials, auxetic reinforcement as well as auxetic composite are under the great attention of scientific research due to having excellent mechanical properties. In the current research work, the impact resistance of composite was improved by modifying the four layer through the thickness woven structure, that was used as reinforcement. A comparison was made between the standard four layer through the thickness and modified four layer through the thickness woven structure in terms of auxeticity and penetration. The modified four layer through the thickness woven structure showed auxetic behavior in both warp and weft direction having less penetration resistance as compared to standard sample. The four layer through the thickness non-auxetic woven structures and modified auxetic four layer thorough the thickness woven structures were used as reinforcements to develop composite for impact resistance comparison. The result showed that impact resistance of auxetic composite was 6.7% greater as compared to the non-auxetic composite. It was concluded that the greater value of impact resistance was due to the auxetic behavior of modified four layer through the thickness woven structure.     

Multi-response optimization of mechanical and comfort properties of bi-stretch woven fabrics using grey relational analysis in Taguchi method

The present paper envisages the multi-response optimization of certain factors like elastane linear density, fabric thread density and weave float on some mechanical, (i.e. stretch %, recovery %) and comfort (i.e. air permeability) properties of bi-stretch woven fabrics, under L₁₈ orthogonal array in Taguchi design. Fabric samples were produced using elastane core-spun cotton yarns both in the warp and weft. The elastane linear density, fabric thread density and weave float size were used as predictor variables, while fabric air permeability, stretch and recovery were taken as response variables. Two different elastane linear densities, i.e. 44 dtex and 78 dtex, 3 different thread densities and 3 different weave designs, i.e. 1/1 plain, 2/2 z-twill and 3/3 z-twill were used. The results have been analysed using grey relational analysis for the identification of an optimum level of process factors. Furthermore, using analysis of variance method, significant contributions of predictor variables were determined.     

Pseudo NLP Joint Spam Classification Technique for Big Data Cluster

Spam mail classification considered complex and error-prone task in the distributed computing environment. There are various available spam mail classification approaches such as the naive Bayesian classifier, logistic regression and support vector machine and decision tree, recursive neural network, and long short-term memory algorithms. However, they do not consider the document when analyzing spam mail content. These approaches use the bag-of-words method, which analyzes a large amount of text data and classifies features with the help of term frequency-inverse document frequency. Because there are many words in a document, these approaches consume a massive amount of resources and become infeasible when performing classification on multiple associated mail documents together. Thus, spam mail is not classified fully, and these approaches remain with loopholes. Thus, we propose a term frequency topic inverse document frequency model that considers the meaning of text data in a larger semantic unit by applying weights based on the document’s topic. Moreover, the proposed approach reduces the scarcity problem through a frequency topic-inverse document frequency in singular value decomposition model. Our proposed approach also reduces the dimensionality, which ultimately increases the strength of document classification. Experimental evaluations show that the proposed approach classifies spam mail documents with higher accuracy using individual document-independent processing computation. Comparative evaluations show that the proposed approach performs better than the logistic regression model in the distributed computing environment, with higher document word frequencies of 97.05%, 99.17% and 96.59%.     

Development & Characterization of Green Composites Using Novel 3D Woven Preforms

Green composites are the emerging materials made using natural fibers and environmentally degradable matrix such as green epoxy. Natural fiber composites are the motivation of researchers for low to medium impact applications as well as structural applications like automobiles. In this research work, 3D orthogonal layer to layer (LL) and through the thickness (TT) woven structures with different interlocking patterns, used as preforms in composites are presented. The mechanical properties of preform as well as associated composites are studied on equivalent fiber volume fraction. Jute yarn was woven into four layered 3D woven structures. The use of bridgeable and sustainable fiber, with its prospective use with the biodegradable matrix, is the objective of this work. The focus of this study is to improve mechanical performance by changing weave pattern, so that the resulting composite is robust in design.     

Investigation of mechanical properties of auxetic woven polymer composite material

Contrary to normal materials, the auxetic/meta‐materials expand transversally as well as longitudinally when subjected to longitudinal load and contract in both directions on compression. In this work, auxeticity is obtained in composites reinforced with carbon woven fabrics placed at certain angles. The auxetic effect on the mechanical properties of the prepared carbon/epoxy composites was investigated. The mechanical properties include the tensile, flexural and impact properties. It is found that the tensile properties are not affected by the auxetic effect; however, the flexural and impact properties are significantly enhanced by the auxetic effect.     

Techno‐mechanical properties of cocoon,raw silk and filament of two mulberry silkworm (Bombyx mori L.) strains

Natural silk is considered as queen of textile due to its superior traits. This study was conducted to assess technical properties of cocoon, and raw silk and mechanical properties of silk filament produced by two mulberry silkworm strains. The Chinese strain (205PO) produced dry cocoon of 0.61±0.04 g with raw silk of 0.30±0.02 g and the Japanese strain (J101) produced dry cocoon of 0.49±0.01 g with raw silk of 0.23±0.00 g. The single filament length of 205PO and J101 was 1203.1±20.42 m cocoon^?1 and 1082.3±48.95 m cocoon^?1, respectively. The filament was finer in the Japanese silkworm strain (1.91±0.06 denier) compared to the Chinese silkworm strain (2.26±0.15 denier). The filament tenacity, tenacity rupture and strain of J101 was 6.24 %, 24.62 % and 4.42 % greater compared to 205PO. The tensile strength of 205PO was 11.82 % greater compared to J101. The filament diameter was 22.01±0.42 μm and 21.98±0.15 μm of 205PO and J101, respectively. Based on these findings, it is recommended that silkworm strains with superior techno‐mechanical properties may be included in breeding programmes for enhancing the quality of silk textile.