Vacuum thermal deposition of crystalline, uniform and stoichiometric CdS thin films in ambient H2S atmosphere

Crystalline, uniform and stoichiometric thin films of CdS have been fabricated on soda lime glass (SLG) substrates using vacuum thermal deposition method in the presence of hydrogen sulphide (H2S) atmosphere. The consequence of ambient H2S on the growth, quality and structure-property relationship of vacuum deposited CdS thin films has been investigated. The deposited films have been characterized by XRD, SEM with EDX analysis, AFM, XPS and optical spectroscopy. The physical characterization of as-deposited CdS films reveals that the films deposited in controlled H2S ambient are more crystalline, highly uniform and stoichiometric in comparison to films deposited without H2S atmosphere

3D Nonwoven Fabrics for Biomedical Applications

Fibrous materials are attractive for biomedical applications owing to their structural superiorities, which include large surface-area-to-volume ratio, high porosity, and pore interconnectivity in a controlled manner. Among the various methods of fiber fabrication, electrospinning has emerged as an attractive nanotechnology to produce ultrafine fibrous materials for myriad applications, including tissue scaffolding. In this technique, processing parameters, such as the solution properties, tip-to-collector distance, applied voltage, etc., can be tailored to obtain the fibers of the desired morphology and physicochemical properties. Ideal scaffolds should meet the basic requirements, such as three-dimensional (3D) architecture, proper mechanical properties and biodegradability, and the sufficient surface characteristics for cell adhesion and proliferation. However, most of the electrospun nanofiber-based scaffolds have densely packed two-dimensional (2D) array which hinders the cell infiltration and growth throughout the scaffolds, thereby limiting their applicability in tissue regeneration. To overcome this problem, several attempts have been made to develop a biomimetic three-dimensional, nanofibrous scaffold. This chapter deals with noble techniques including gas foaming (GF), charge repulsion-assisted fabrication, post-processing, liquid-assisted collection, collector modification, and porogen-assisted methods for the fabrication of 3D nanofibrous scaffold for biomedical applications

DASMcC: Data Augmented SMOTE Multi-Class Classifier for Prediction of Cardiovascular Diseases Using Time Series Features

One of the leading causes of mortality worldwide is cardiovascular disease (CVD). Electrocardiography (ECG) is a noninvasive and cost-effective tool to diagnose the heart’s health. This study presents a multi-class classifier for the prediction of four different types of Cardiovascular Diseases, i.e., Myocardial Infarction, Hypertrophy, Conduction Disturbances, and ST-T abnormality using 12-lead ECG. There are four key steps involved in the presented work: data preprocessing, feature extraction, data preparation, and augmentation, and modelling for multi-class CVD classification. The sixteen-time domain augmented features are used to train the classifier. The work is divided into three parts: extracting the features from raw 12-lead ECG signals, data preparation and augmentation, and training, testing, and validating the classifier. A comparative study of the performance of five different classifiers (i.e., Random Forest (RF), K Nearest Neighbors (KNN), Gradient Boost, Adda Boost, and XG Boost has also been presented. Accuracy, precision, recall, and F1 scores are used for performance evaluation. Further, the Receiver Operating Curve (ROC) is traced, and the Area Under the Curve (AUC) is calculated to ensure the unbiased performance of the classifier. The application of the proposed classifier in the Smart Healthcare framework has also been discussed.publishedVersio

Fixed and coincidence point theorems on partial metric spaces with an application

The aim of this paper is to investigate some fixed and co-incidence point theorems in complete, orbitally complete and (T, f)-orbitally complete partial metric spaces under the generalized contractive type conditions of mappings. Moreover, our results generalize and extend the several obtained results in the literature. Additionally some non-trivial examples are demonstrated, and an application has discussed to integral equations

Smart Grid Sensor Monitoring Based on Deep Learning Technique with Control System Management in Fault Detection

The smart grid environment comprises of the sensor for monitoring the environment for effective power supply, utilization and establishment of communication. However, the management of smart grid in the monitoring environment isa difficult process due to diversifieduser request in the sensor monitoring with the grid-connected devices. Presently, context-awaremonitoring incorporates effective management of data management and provision of services in two-way processing and computing. In a heterogeneous environment context-aware, smart grid exhibits significant performance characteristics with the grid-connected communication environment for effective data processing for sustainability and stability. Fault diagnoses in the automated system are formulated to diagnose the fault separately. This paper developed anoptimized power grid control model (OPGCM) model for fault detection in the control system model for grid-connected smart home appliances. OPGCM model uses the context-aware power-awarescheme for load management in grid-connected smart homes. Through the adaptive smart grid model,power-aware management is incorporated with the evolutionary programming model for context-awareness user communication. The OPGCM modelperforms fault diagnosis in the grid-connected control system initially, Fault diagnosis system comprises of a sequential process with the extraction of the statistical features to acquirea sustainable dataset with effective signal processing. Secondly, the features are extracted based on the sequential process for the acquired dataset with a reduction of dimensionality. Finally, the classification is performed with the deep learning model to predict or identify the fault pattern. With the OPGCM model, features are optimized with the whale optimization model to acquire features to perform fault diagnosis and classification. Simulation analysis expressed that the proposed OPGCM model exhibits ~16% improved classification accuracy compared with the ANN and HMM model

Nanostructured Manganese Hexacyanidocobaltate(III) as Heterogeneous Catalyst for Solvent-Free Oxidation of Benzyl Alcohol

Manganese hexacyanidocobaltate(III) nanostructures have been synthesized using double decomposition method and characterized in terms of CHNS analysis, atomic absorption spectroscopy, thermal analysis, infrared spectral studies and transmission electron microscopic technique. The transmission electron microscopic image of the synthesized material showed that it is consisting of roughly spherical shaped particle with size range of 30-60 nm. The catalytic activity of the synthesized material was studied for the oxidation of benzyl alcohol, using H2O2 as oxidant, under solvent-free conditions. The characterization of the oxidation product of benzyl alcohol and its quantitative estimation was done using gas chromatography. The synthesized material was found to be an effective heterogeneous catalyst with a high selectivity towards benzaldehyde as the oxidation product. It showed 31% conversion of benzyl alcohol under the optimized conditions of various reaction parameters, namely, amount of catalyst, reaction temperature, benzyl alcohol to H2O2 molar ratio and the reaction time

A systematic study on material properties of water retted Sterculia and Bauhinia fiber

Lignocellulose biomass forms an important component of traditional and next generation composite materials. To obtain desired properties, the biomass needs to be chemo‒mechanically processed at different levels. The raw lignocellulose fiber obtained from Sterculia villosa (Roxb.) and Bauhinia vahlii is traditionally believed to have high water stability; and therefore used in rural areas of South Asian regions to secure objects submerged under water. In this research, we systematically studied several material properties of raw Sterculia and Bauhinia fiber samples retted for 0, 20, 30 and 55 days (n=8). Water retting resulted in significant decrease in lignin and extractives content (p0.05). Interestingly, water retting resulted in increased thermal stability in both fiber types. These findings suggested that the fiber studied have excellent water stability. The observed trend in mechanical and thermal properties could have resulted from crystallinity change and/or nominal fiber damage as supported by XRD and SEM imaging data; respectively. These findings suggested that Sterculia and Bauhinia fiber biomass could be an important component of biodegradable composite materials which are intended for high wetting and/or humid conditions

A systematic study on material properties of water retted Sterculia and Bauhinia fiber

Lignocellulose biomass forms an important component of traditional and next generation composite materials. To obtain desired properties, the biomass needs to be chemo‒mechanically processed at different levels. The raw lignocellulose fiber obtained from Sterculia villosa (Roxb.) and Bauhinia vahlii is traditionally believed to have high water stability; and therefore used in rural areas of South Asian regions to secure objects submerged under water. In this research, we systematically studied several material properties of raw Sterculia and Bauhinia fiber samples retted for 0, 20, 30 and 55 days (n=8). Water retting resulted in significant decrease in lignin and extractives content (p0.05). Interestingly, water retting resulted in increased thermal stability in both fiber types. These findings suggested that the fiber studied have excellent water stability. The observed trend in mechanical and thermal properties could have resulted from crystallinity change and/or nominal fiber damage as supported by XRD and SEM imaging data; respectively. These findings suggested that Sterculia and Bauhinia fiber biomass could be an important component of biodegradable composite materials which are intended for high wetting and/or humid conditions

Redescription of the enigmatic jellyfish, Crambionella annandalei (Cnidaria: Scyphozoa) from Indian waters

The catostylid jellyfish, Crambionella annandalei was originally described by Rao (1932) based on a preserved specimen collected from the Andaman Sea. Since then, no detailed taxonomic studies have been conducted and the species is often misidentified. Here, we provide a detailed morphological re-description of C. annandalei from fresh material collected at a variety of locations along the east coast of India. The species can be distinguished from its congeners by a combination of morphological characters, the most important of which are the proportion of terminal club length to oral arm length (0.48 ± 0.031), the proportion of the distal portion of the oral arm to naked proximal portion (7.25 ± 0.268) and the body colour. The occurrence of intra-specific colour variation in fresh specimens was also observed in the present study