Tuning of shear thickening behavior and elastic strength of polyvinylidene fluoride via doping of ZnO-graphene

ZnO rice like nonarchitects are grafted on the graphene carbon core via a rapid microwave synthesis route. The prepared grafted systems are characterized via XRD, SEM, RAMAN, and XPS to examined the structural and morphological parameters. Zinc oxide grafted graphene sheets (ZnO-G) are further doped in β-phase of polyvinylidene fluoride (PVDF) to prepare the polymer nanocomposites (PNCs) via mixed solvent approach (THF/DMF). β-phase confirmation of PVDF PNCs is done by FTIR studies. It is observed that ZnO-G filler enhances the β-phase content in the PNCs. Non-doped PVDF and PNCs are further studied for rheological behavior under the shear rate of 1–100 s⁻¹. Doping of ZnO-G dopant to the PVDF matrix changes its discontinuous shear thickening (DST) behavior to continues shear thickening behavior (CST). Hydrocluster formation and their interaction with the dopant could be the reason for this striking DST to CST behavioral change. Strain amplitude sweep (10⁻³% -10%) oscillatory test reveals that the PNCs shows extended linear viscoelastic region with high elastic modulus and lower viscous modulus. Effective shear thickening behavior and strong elastic strength of these PNCs present their candidature for various fields including mechanical and soft body armor applications.     

Facial expression recognition using modified Viola-John’s algorithm and KNN classifier

Multimedia Tools and Applications - In the way of communication, facial expression act as non-verbal communication and play an important role in social interaction by providing some contextual...     

Performance of a non‐load‐bearing steel stud gypsum board wall assembly: Experiments and modelling

A gypsum wall assembly was exposed to an intense real‐scale compartment fire. For the wall assembly, temperatures were measured at the exposed face, within the stud cavity, and at the unexposed face during the fire exposure. Total heat flux gauges were used to measure the temporal variation of the energy incident on the walls, and cameras, both visual and infrared, were used to image the unexposed face of the wall assembly during the fire exposure. The behaviour of the wall assembly under the fire load is discussed as are current model results for a simulation of the fire test. Copyright © 2006 John Wiley & Sons, Ltd.     

Sentiment Analysis with Tweets Behaviour in Twitter Streaming API

Twitter is a radiant platform with a quick and effective technique to analyze users’ perceptions of activities on social media. Many researchers and industry experts show their attention to Twitter sentiment analysis to recognize the stakeholder group. The sentiment analysis needs an advanced level of approaches including adoption to encompass data sentiment analysis and various machine learning tools. An assessment of sentiment analysis in multiple fields that affect their elevations among the people in real-time by using Naive Bayes and Support Vector Machine (SVM). This paper focused on analysing the distinguished sentiment techniques in tweets behaviour datasets for various spheres such as healthcare, behaviour estimation, etc. In addition, the results in this work explore and validate the statistical machine learning classifiers that provide the accuracy percentages attained in terms of positive, negative and neutral tweets. In this work, we obligated Twitter Application Programming Interface (API) account and programmed in python for sentiment analysis approach for the computational measure of user’s perceptions that extract a massive number of tweets and provide market value to the Twitter account proprietor. To distinguish the results in terms of the performance evaluation, an error analysis investigates the features of various stakeholders comprising social media analytics researchers, Natural Language Processing (NLP) developers, engineering managers and experts involved to have a decision-making approach.     

Fuzzy and Deep Belief Network Based Malicious Vehicle Identification and Trust Recommendation Framework in VANETs

Wireless Personal Communications - This work aims to implement a clustering scheme to separate vehicles into a cluster that is based on various parameters, such as the total number of relay nodes,...     

Agent-based negotiation and decision making for dynamic supply chain formation

Modern businesses are facing the challenge of effectively coordinating their supply chains from upstream to downstream services. It is a complex problem to search, schedule, and coordinate a set of services from a large number of service resources under various constraints and uncertainties. Existing approaches to this problem have relied on complete information regarding service requirements and resources, without adequately addressing the dynamics and uncertainties of the environments. The real-world situations are complicated as a result of ambiguity in the requirements of the services, the uncertainty of solutions from service providers, and the interdependencies among the services to be composed. This paper investigates the complexity of supply chain formation and proposes an agent-mediated coordination approach. Each agent works as a broker for each service type, dedicated to selecting solutions for each service as well as interacting with other agents in refining the decision making to achieve compatibility among the solutions. The coordination among agents concerns decision making at strategic, tactical, and operational level. At the strategic level, agents communicate and negotiate for supply chain formation; at the tactical level, argumentation is used by agents to communicate and understand the preferences and constraints of each other; at the operational level, different strategies are used for selecting the preferences. Based on this approach, a prototype has been implemented with simulated experiments highlighting the effectiveness of the approach.     

High-pressure release and dispersion of hydrogen in a partially enclosed compartment: Effect of natural and forced ventilation

The study of compressed hydrogen releases from high-pressure storage systems has practical application for hydrogen and fuel cell technologies. Such releases may occur either due to accidental damage to a storage tank, connecting piping, or due to failure of a pressure release device (PRD). Understanding hydrogen behavior during and after the unintended release from a high-pressure storage device is important for development of appropriate hydrogen safety codes and standards and for the evaluation of risk mitigation requirements and technologies. In this paper, the natural and forced mixing and dispersion of hydrogen released from a high-pressure tank into a partially enclosed compartment is investigated using analytical models. Simple models are developed to estimate the volumetric flow rate through a choked nozzle of a high-pressure tank. The hydrogen released in the compartment is vented through buoyancy induced flow or through forced ventilation. The model is useful in understanding the important physical processes involved during the release and dispersion of hydrogen from a high-pressure tank into a compartment with vents at multiple levels. Parametric studies are presented to identify the relative importance of various parameters such as diameter of the release port and air changes per hour (ACH) characteristic of the enclosure. Compartment overpressure as a function of the size of the release port is predicted. Conditions that can lead to major damage of the compartment due to overpressure are identified. Results of the analytical model indicate that the fastest way to reduce flammable levels of hydrogen concentration in a compartment is by blowing through the vents. Model predictions for forced ventilation are presented which show that it is feasible to effectively and rapidly reduce the flammable concentration of hydrogen in the compartment following the release of hydrogen from a high-pressure tank.     

Studies for Wear Property Correlation for Carbon Fabric-Reinforced PES Composites

Polyethersulfone (PES) composites were developed with carbon fabric (CF). Cold remote nitrogen oxygen plasma (CRNOP) treatment was employed to the CF to incorporate functional groups and promote fiber–matrix adhesion. This study includes the effect of PES melt flow index (MFI) on the wettability of CF and its influence on fretting wear performance. Evaluations of fretting wear properties of composites led to the conclusion that the CRNOP treatment proved beneficial to enhance performance properties significantly. Polymer MFI and treatment to CF proved to be the decisive parameters for controlling performance of composites apart from operating parameters. Perforations on the treated carbon fiber, evidently observed by FESEM, improved the fiber–matrix adhesion, and hence the performance properties. Artificial neuron network (ANN) was used for prediction of the wear behavior of composites.