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A Study of the Thermo-physiological Comfort Properties of Fabrics treated with Neem and Bermuda grass Herbal Finishes
Get PDFComfort is an aspect and is considered as one of the important characteristics of clothing. Generally, the comfort properties can be distinguished into three categories, viz., thermo-physiological comfort, sensorial comfort, and psychological comfort. Thermo-physiological comfort of a fabric is determined by the ability of the fabric to transmit heat, air, and moisture from the skin to the atmosphere. Fabric made of synthetic fibers has convincing comfort properties when compared with the fabrics made of natural fibers like cotton, silk, wool, etc. When natural fibers are compared with synthetic fibers, synthetic fibers are hydrophobic in nature and provides less comfort to the wearer. Hence, in this research work, an attempt has been made to study the thermo-physiological comfort properties of herbal finished woven fabrics made from synthetic fibers coated with neem and bermudagrass. Herbal finishes were applied on the woven fabrics made from 100% polyester and blends of 50:50 polyester, acrylic fibers. These finished fabrics were tested and analyzed for durability and essential thermo-physiological comfort properties. Based on the test results and analysis it was found that significant improvements in the moisture-related properties and moderate decrease in the thermal conductivity of the synthetic fabrics with neem and bermudagrass herbal finish
Comparative studies on thermal comfort properties of Eri silk, Wool/Eri silk, Cotton and Micro-denier Acrylic double-layered knitted fabrics
Get PDFIn this study, 100% Eri Silk, 85:15% Wool/Eri silk, Cotton, and Micro-denier Acrylic yarns have been used to develop double-layered knitted fabrics. The thermal comfort properties such as air permeability, thermal resistance, thermal conductivity, and water vapor transmission have been evaluated. In this study, it is found that 100% eri silk double-layered knitted fabrics have better air permeability properties when compared with the other fabrics (85:15% Wool/Eri silk and 100% micro-denier acrylic fabrics) due to their thin and porous structure. As far as thermal conductivity is concerned, 100% Eri silk double-layered knitted fabrics have better thermal conductivity, whereas 85:15% wool/Eri silk double-layered knitted fabrics show poor thermal conductivity. In the case of thermal resistance, 85:15% wool/Eri silk double-layered knitted fabrics have maximum thermal resistance and 100% Eri silk shows poor thermal resistance when compared to other samples. A total of 100% Eri silk double-layered knitted fabrics have better water vapor transmission and 85:15% wool/Eri silk double-layered knitted fabrics have poor water vapor transmission when compared to other samples
Effect of Musa acuminata SAP on Whiteness Index and Physical Properties of Cotton Finished Fabrics
Get PDFThirty-six samples of woven fabric were finished with Musa acuminata SAP in three different concentrations, viz., 50%, 75%, and 100% were applied on cotton fabrics using padding mangle at four different temperatures, viz., room temperature, 50°C, 75°C, and 100°C and also with three different process timings, viz., 20 min, 40 min, and 60 min, respectively. The finished fabrics have been analyzed on the whiteness and yellow index properties. It has been observed that the fabric finished with a 50% concentration of Musa acuminata SAP with 50°C for a prolonged time of processing shows a lesser yellow index and higher whiteness index value followed by the 100% and 75% concentration of SAP. The finished optimized fabric samples have been evaluated on the physical properties of fabric such as tensile strength, tear strength, pilling, drape coefficient, and stiffness of the fabric. It has been found that the fabric finished with a 50% concentration of Musa acuminata SAP with the lowest temperature at least processing time has better performance in the physical properties of the fabric when compared to the highest concentration of Musa acuminata SAP with the highest temperature at highest process timing
Development of Magnetic Nanoparticles and Encapsulation Methods – An Overview
Get PDFNanotechnology is a versatile evolving field today. The importance of nanoparticles reaches high in diagnostics, medicine, or pharmaceuticals for drug delivery. Among all the different nanoparticles, Magnetic nanoparticles are novel, easily prepared, and have many biomedical applications. The specific character of Magnetic nanoparticles shows various applications like a diagnosis of diseases, targeted drug delivery, and cancer therapy. An overview of this topic includes all about the history, advantages, disadvantages, preparation methods, and biomedical applications of Magnetic nanoparticles. It also focuses on the overall information of Magnetic nanoparticles and their prospective, challenges faced in the delivery of drugs have also been discussed
Atomistic simulation on flavonoids derivatives as potential inhibitors of bacterial gyrase of Staphylococcus aureus
No full textABSTRACT
The bacterial DNA gyrase is an attractive target to identify the novel antibacterial agents. The flavon�oid derivatives possess various biological activities such as antimicrobial, anti-inflammatory and anti�cancer activities. The aim of present study is to identify the potential molecule from flavonoid
derivatives against Staphylococcus aureus using atomistic simulation namely Molecular Docking, Quantum Chemical and Molecular Dynamics. The molecules Cpd58, Cpd65 and Cpd70 are identified as potential molecules through molecular docking approaches by exploring through the N – H …O hydrogen bonding interactions with Asn31 and Glu35 of Gyrase B. To confirm the intramolecular charge transfer in the flavonoid derivatives, Frontier Molecular Orbital (FMO) calculation was per�formed at M06/6-31g(d) level in gas phase. The lowest HOMO-LUMO gap was calculated for Cpd58, Cpd65 and Cpd70 among the selected compounds used in this study. Molecular dynamics simulation were carried out for Cpd58 and Cpd70 for a time period of 50 ns and found to be stable throughout
the analysis. Therefore, the identified compounds are found to be a potent inhibitor for GyrB of S. aur�eus that can be validated by experimental studies
Online monitoring of crack depth in Fiber Reinforced Composite Beams using optimization Grey model GM(1,N),
No full textThe existing analytical and numerical simulation models are not able to estimate the crack depth when poor information available about the crack. The present study is aimed to develop an online monitoring system to estimate crack depth in composites. The online monitoring system is developed with optimization Grey model OGM(1,N) and support vector machine (SVM) separately and the crack depth is estimated in E-glass fiber reinforcement polymer composites. In this study, cracks are made artificially on the E-glass fiber reinforcement polymer at distance of 50, 100 and 150 mm from free end with crack depth ratios of 12.9%, 14.1%, 15.3%, 16.5%, 17.6%
and 18.8%. Natural frequency is measured at three nodes for all the cracks. The proposed SVM and GN(1,N) models are trained with four samples for each position and tested for the remaining
two samples. In the proposed OGM(1,N) model, training samples are updated by adding recent data sample and deleting old data. So that the OGM(1,N) model predicts the crack depth ratio
more accurately than the SVM model with an error of 1.06%. The OGM (1, N) is simple and directly estimates the crack depth by taking into account online measured data of the vibration
frequency. Based on the accuracy in prediction, the Grey online modeling and monitoring system is suggested to estimated crack depth in composites. Interaction effect of the crack position and crack depth ratio on the natural frequency at the three nodes is studied
Performance evaluation of DNN with other machine learning techniques in a cluster using Apache Spark and MLlib
Get PDFSentiment analysis on large data has become challenging due to the diversity, and nature of data. Advancements in the internet, along with large data availability have obviated the traditional limitations to distributed computing. The objective of this work is to carry out sentiment analysis on Apache Spark distributed Framework to speed up computations and enhance machine performance in diverse environ-ments. The analysis, such as polarity identification, subjective analysis and email spam etc., are carried on various text datasets. After pre-processing, Term Frequency-Inverse Document Frequency (TF-IDF) and
unsupervised Spark-Latent Dirichlet Allocation (LDA) clustering algorithms are used for feature extrac-tion and selection to improve the accuracy. Deep Neural Networks (DNN), Support Vector Machines (SVM), Tree ensemble classifiers are used to evaluate the performance of the framework on single node
and cluster environments. Finally, the proposed work aims at building an approach for enhancing machine performance, more in terms of runtime over accuracy
FERNet: A Deep CNN Architecture for Facial Expression Recognition in the Wild
Get PDFFacial expression recognition is an intriguing and demanding subject in the realm of computer vision. In this paper, we propose a novel deep learning-based strategy to address the challenges of facial expression recognition from images. Our model is developed in such a manner that it learns hidden nonlinearity from the input facial images, which is critical for discriminating the type of emotion a person is expressing. We developed a deep convolutional neural network model composed of a sequence of blocks, each consists of multiple convolutional layers and sub-sampling layers. Investigations on the benchmark FER2013 dataset indicate that the proposed facial expres-sion recognition network (FERNet) surpasses existing approaches in terms of performance and model complexity. We trained our model on the FER2013 dataset, which is the most challenging of all the available datasets for this task, and achieve an accuracy of around 69.57%. Furthermore, we investigate the effects of dropout, batch normalization, and augmentation, as well as how they aid in the reduction of over-fitting and improved performance
Hybrid Atom Search-Heap Energy Optimization Algorithm for Dynamic Topology in Underwater Acoustic Sensor Network
Get PDFBenefits obtained from the water resources are many in terms of renewable energy, food, materials, communication and security. Monitoring, sensing, routing and analysis of underwater happenings is the challenging task in a sparse environment. Due to the dynamic nature of underwater topology, routing among the nodes is a complex task, hence optimal node identification is mandatory for enhancing network performance. The environment and vast area of ocean makes it highly unlikely for humans to explore and monitor as a challenging task and more cost then manual labour. Hence, economical solutions for monitoring and exploration applications in the ocean are possible with Underwater Acoustic Sensor Network (UWASN). This work proposes a meta-heuristic novel hybrid Atom searchHeap optimization (NHASHO) algorithm to enhance the network performance in terms of energy, end-to-end delay and throughput. The NS2 simulation results show that the proposed work outperforms with existing Cat Optimization algorithm (CAO) for dynamically varying underwater topology condition
Feature Extraction using LSTM Autoencoder in Network Intrusion Detection System
Get PDFWith the intent growth of web-based data, document classification has become an important task that can be used in many real-time applications to handle and organize text documents. In the traditional approaches, text documents are encoded using fixed length feature vector representation. Compared to the static, fixed length representation, a text document can be better represented using variable length feature vector representation, where text documents are allowed to have variable number of features. In this paper, SVM-based classification is used as it does not suffer much from the curse of dimensionality. User-defined kernel functions such as Jaccard coefficient kernel, n-gram kernel, and string subsequence kernels are used to find the kernel value between a pair of documents. To prove the performance of the proposed method, benchmark datasets like Reuters-21578 and Reuters-8, the most widely used datasets for text classification, are used for our experimental studies. Based on our experimental studies,
we claim that SVM using N-gram kernel gives better performance on Reuters-21578 dataset and SVM using string
subsequence kernel gives better performance on Reuters-8 dataset. We also observed that minor modifications to the
user-defined kernel improve the performance of the model