(BaTiO3)1-x + (Co0.5Ni0.5Nb0.06Fe1.94O4)x nanocomposites: Structure,morphology, magnetic and dielectric properties

Two phase-based nanocomposites consisting of dielectric barium titanate (BaTiO₃ or BTO) and magnetic spinel ferrite Co_0.5Ni_0.5Nb_0.06Fe_1.94O₄ (CNNFO) have been synthesized through solid state route. Series of (BaTiO₃)_1-x + (Co_0.5Ni_0.5Nb_0.06Fe_1.94O₄)_x nanocomposites with x content of 0.00, 0.25, 0.50, 0.75, and 1.00 were considered. The structure has been examined via X-rays diffraction (XRD) and indicated the occurrence of both perovskite BTO and spinel CNNFO phases in various nanocomposites. A phase transition from tetragonal BTO structure to cubic structure occurs with inclusion of CNNFO phase. The average crystallites size of BTO phase decreases, whereas that for the CNNFO phase increases with increasing x in various nanocomposites. The morphological observations revealed that the porosity is highly reduced, and the connectivity between grains is enhanced with increasing x content. The optical properties have been investigated by UV−vis diffuse reflectance spectroscopy. The deduced band gap energy (E_g) value is found to reduce with increasing the content of spinel ferrite phase. The magnetic as well as the dielectric properties were also investigated. The analysis showed that CNNFO ferrite phase greatly affects the magnetic properties and dielectric response of BTO material. The obtained findings can be useful to enhance the performances of magneto-dielectric composite-based systems.     

Vapor Shield Models for Fusion Reactors Plasma-Facing Components

Radiant heat flux is a dominant mechanism by which energy transfers from the high-temperature core plasma to the interior critical components of the fusion reactor, which result in surface ablation and sever damage to the components. A vapor layer develops at the surface and provides a self-shielding mechanism at the plasma-material interface. Two models for the energy transmission factor through the boundary layer were developed and incorporated in the electrothermal plasma capillary code to predict the effectiveness of these models in surface self-protection. The electrothermal plasma capillary discharge simulates the typical conditions of fusion reactors disruption and quench phase and has been shown to be an adequate technique to evaluate the erosion of plasma-facing component. First model treats the radiant heat transport as it is affected by the variation of the plasma opacity, in which the vapor shield efficiency depends on the plasma optical thickness and the mean plasma opacity. The second model defines the vapor shield by the ratio of the energy reaching the surface to the total radiant energy emitted by the plasma with the inclusion of the plasma kinetic energy. The code can predict the axial and temporal variation of the transmission factor at each time step and mesh point, and predicts the plasma parameters with the effectiveness of the vapor shield at the boundary layer. The code prediction with implementation of both models has been used to compare the results with earlier ones and with some experimental data. Code results are in good correlation with experimentally measured ablation data.     

An empirical study of factors influencing cloud adoption among private sector organisations

In many developing countries such as Saudi Arabia the adoption of cloud computing is still at an early stage. This research aims to investigate the influencing factors in the decision to adopt cloud computing in the private sector. An integrated model is proposed incorporating critical factors derived from a literature review, along with other factors (such as physical location) that have not been examined in previous studies as main factors in the organisation’s decision to adopt cloud services. Data were collected from 300 IT staff in different organisations in the private sector in Saudi Arabia, in order to test the cloud adoption model and explore factors that were positively or negatively associated with cloud adoption. The most influential determinants of cloud adoption were found to be quality of service and trust. However, security and privacy concerns still prevent cloud adoption in this country. This study also showed that the effect of these variables differed according to organisation size and in adopter and non-adopter companies. Overall, these research findings provide valuable guidelines to cloud providers, managers, and government policy makers on ways of encouraging the spread of cloud computing in Middle Eastern countries and increasing its implementation, particularly in Saudi Arabia.     

Anti-Hepatocellular Carcinoma Biomolecules: Molecular Targets Insights

This report explores the available curative molecules directed against hepatocellular carcinoma (HCC). Limited efficiency as well as other drawbacks of existing molecules led to the search for promising potential alternatives. Understanding of the cell signaling mechanisms propelling carcinogenesis and driven by cell proliferation, invasion, and angiogenesis can offer valuable information for the investigation of efficient treatment strategies. The complexity of the mechanisms behind carcinogenesis inspires researchers to explore the ability of various biomolecules to target specific pathways. Natural components occurring mainly in food and medicinal plants, are considered an essential resource for discovering new and promising therapeutic molecules. Novel biomolecules normally have an advantage in terms of biosafety. They are also widely diverse and often possess potent antioxidant, anti-inflammatory, and anti-cancer properties. Based on quantitative structure–activity relationship studies, biomolecules can be used as templates for chemical modifications that improve efficiency, safety, and bioavailability. In this review, we focus on anti-HCC biomolecules that have their molecular targets partially or completely characterized as well as having anti-cancer molecular mechanisms that are fairly described.     

Counting-based secret sharing technique for multimedia applications

Multimedia Tools and Applications - Secret Sharing is required in situations where access to important resources has to be protected by more than one person. We propose new secret-sharing scheme...     

Antibacterial and Antibiofilm Activity of Mercaptophenol Functionalized-Gold Nanorods Against a Clinical Isolate of Methicillin-Resistant Staphylococcus aureus

Journal of Inorganic and Organometallic Polymers and Materials - Gold nanorods (AuNRs) were synthesized by the seed-mediated wet chemical method using a binary surfactant system. AuNRs were...     

Dynamic Hand Gesture Recognition Using 3D-CNN and LSTM Networks

Recognition of dynamic hand gestures in real-time is a difficult task because the system can never know when or from where the gesture starts and ends in a video stream. Many researchers have been working on vision-based gesture recognition due to its various applications. This paper proposes a deep learning architecture based on the combination of a 3D Convolutional Neural Network (3D-CNN) and a Long Short-Term Memory (LSTM) network. The proposed architecture extracts spatial-temporal information from video sequences input while avoiding extensive computation. The 3D-CNN is used for the extraction of spectral and spatial features which are then given to the LSTM network through which classification is carried out. The proposed model is a light-weight architecture with only 3.7 million training parameters. The model has been evaluated on 15 classes from the 20BN-jester dataset available publicly. The model was trained on 2000 video-clips per class which were separated into 80% training and 20% validation sets. An accuracy of 99% and 97% was achieved on training and testing data, respectively. We further show that the combination of 3D-CNN with LSTM gives superior results as compared to MobileNetv2 + LSTM.     

Surfactant-free synthesis of poly (styrene-co-acrylamide) monodisperse nanoparticles using hybrid flow-to-batch chemistry

We have demonstrated a hybrid flow-to-batch process for synthesizing monodisperse poly(styrene-co-acrylamide) nanoparticles via a surfactant-free emulsion radical polymerization. The flow-to-batch synthesized nanoparticles have a smaller average particle size, tighter particle size distribution, higher molecular weight, and lower molecular weight distribution compared to conventionally batch-synthesized nanoparticles. Our results also indicate that the flow-to-batch synthesized nanoparticles have more hydrophilic acrylamide segments on the particle surface than the batch-synthesized nanoparticles. These results demonstrate that a flow synthesis process can improve the quality of nanoparticles due to the efficient mixing and heat transfer in a flow reactor and simplify the scale up of nanoparticle synthesis in a conventional chemistry lab.     

Machine Learning Based Cybersecurity Threat Detection for Secure IoT Assisted Cloud Environment

The Internet of Things (IoT) is determine enormous economic openings for industries and allow stimulating innovation which obtain between domains in childcare for eldercare, in health service to energy, and in developed to transport. Cybersecurity develops a difficult problem in IoT platform whereas the presence of cyber-attack requires that solved. The progress of automatic devices for cyber-attack classifier and detection employing Artificial Intelligence (AI) and Machine Learning (ML) devices are crucial fact to realize security in IoT platform. It can be required for minimizing the issues of security based on IoT devices efficiently. Thus, this research proposal establishes novel mayfly optimized with Regularized Extreme Learning Machine technique called as MFO-RELM model for Cybersecurity Threat classification and detection from the cloud and IoT environments. The proposed MFO-RELM model provides the effective detection of cybersecurity threat which occur in the cloud and IoT platforms. To accomplish this, the MFO-RELM technique pre-processed the actual cloud and IoT data as to meaningful format. Besides, the proposed models will receive the pre-processing data and carry out the classifier method. For boosting the efficiency of the proposed models, the MFO technique was utilized to it. The experiential outcome of the proposed technique was tested utilizing the standard CICIDS 2017 dataset, and the outcomes are examined under distinct aspects.