Dynamic analysis and drilling degrees of freedom

Finite element methods for dynamic analysis employing elements with drilling degrees of freedom are presented. The formulation is based on a variational principle in which displacements and rotations are interpolated independently. The issue of zero masses corresponding to rotational degrees of freedom is addressed and techniques for defining consistent and lumped rotational mass matrices are presented.     

A knowledge-based system for sensor selection

This paper presents a Knowledge-based system (KBS) developed to allow users, who may not be knowledgeable about sensors, to select sensors suitable for their specific needs. The KBS runs on a micro-computer. The selection criteria are user specified and are based on the desired measurement parameters. The system output includes all of the operational and dimensional parameters of the recommended sensor, price, and vendor information.     

Ellipsoidal neighbourhood outlier factor for distributed anomaly detection in resource constrained networks

Anomaly detection in resource constrained wireless networks is an important challenge for tasks such as intrusion detection, quality assurance and event monitoring applications. The challenge is to detect these interesting events or anomalies in a timely manner, while minimising energy consumption in the network. We propose a distributed anomaly detection architecture, which uses multiple hyperellipsoidal clusters to model the data at each sensor node, and identify global and local anomalies in the network. In particular, a novel anomaly scoring method is proposed to provide a score for each hyperellipsoidal model, based on how remote the ellipsoid is relative to their neighbours. We demonstrate using several synthetic and real datasets that our proposed scheme achieves a higher detection performance with a significant reduction in communication overhead in the network compared to centralised and existing schemes.     

Alpha Fusion Adversarial Attack Analysis Using Deep Learning

The deep learning model encompasses a powerful learning ability that integrates the feature extraction, and classification method to improve accuracy. Convolutional Neural Networks (CNN) perform well in machine learning and image processing tasks like segmentation, classification, detection, identification, etc. The CNN models are still sensitive to noise and attack. The smallest change in training images as in an adversarial attack can greatly decrease the accuracy of the CNN model. This paper presents an alpha fusion attack analysis and generates defense against adversarial attacks. The proposed work is divided into three phases: firstly, an MLSTM-based CNN classification model is developed for classifying COVID-CT images. Secondly, an alpha fusion attack is generated to fool the classification model. The alpha fusion attack is tested in the last phase on a modified LSTM-based CNN (CNN-MLSTM) model and other pre-trained models. The results of CNN models show that the accuracy of these models dropped greatly after the alpha-fusion attack. The highest F1 score before the attack was achieved is 97.45 And after the attack lowest F1 score recorded is 22%. Results elucidate the performance in terms of accuracy, precision, F1 score and Recall.     

A Multiscale/stabilized Formulation of the Incompressible Navier–Stokes Equations for Moving Boundary Flows and Fluid–structure Interaction

This paper presents a multiscale/stabilized finite element formulation for the incompressible Navier–Stokes equations written in an Arbitrary Lagrangian–Eulerian (ALE) frame to model flow problems that involve moving and deforming meshes. The new formulation is derived based on the variational multiscale method proposed by Hughes (Comput Methods Appl Mech Eng 127:387–401, 1995) and employed in Masud and Khurram in (Comput Methods Appl Mech Eng 193:1997–2018, 2006); Masud and Khurram in (Comput Methods Appl Mech Eng 195:1750–1777, 2006) to study advection dominated transport phenomena. A significant feature of the formulation is that the structure of the stabilization terms and the definition of the stabilization tensor appear naturally via the solution of the sub-grid scale problem. A mesh moving technique is integrated in this formulation to accommodate the motion and deformation of the computational grid, and to map the moving boundaries in a rational way. Some benchmark problems are shown, and simulations of an elastic beam undergoing large amplitude periodic oscillations in a viscous fluid domain are presented.     

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.     

High dielectric permittivity and percolative behavior of polyvinyl alcohol/potassium dihydrogen phosphate composites

A new kind of anhydrous, transparent, and flexible potassium dihydrogen phosphate (KH₂PO₄ or KDP)/polyvinyl alcohol (PVA) composite in the form of film (0.10 mm) has been prepared by solution casting technique. KDP is well dispersed in the polymer matrix as observed from the microstructural studies. Frequency and temperature dependent dielectric properties of the composites have been studied with varying KDP concentrations. The PVA/KDP composite films exhibited extraordinarily high relative permittivity ε′ ∼ 430 (80 times higher compared with pure PVA and even higher than KDP) near the percolation threshold (ϕ_C = 2.5 wt % KDP) with low dielectric losses (∼ 0.15) at 1 kHz and room temperature. Such flexible, low loss and high dielectric permittivity material has enormous importance for application in devices. © 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012     

Prospects of non-edible neem (Azadirachta indica) oil in Bangladesh: performance and emission evaluation in a direct injection diesel engine

Transition to alternative energy is receiving serious attention globally and in this very way, biodiesel from inedible neem can be an alternative to conventional liquid fuels as it contains 30–45%?wt of oil. In this study, esterification and transesterification reaction of methanol and triglycerides at a molar ratio of 7:1 was done and maximum 96% by volume biodiesel was extracted. Physiochemical properties of neem biodiesel lies within ASTM standards and Fourier transform infrared spectroscopy analysis of neem biodiesel reveals the presence of strong esters and the alkane group. The maximum efficiencies were found to be 22.8% and 22.3%, whereas minimum brake specific fuel consumptions were observed to be 0.57 and 0.59?kg/kW-hr for diesel and B20, respectively. Moreover, B20 provides less emission of HC, CO_x and increase in NO_x. Taking all into consideration, if at least 60% of the arid lands is utilised to cultivate neem trees then it will minimise the import of fossil fuel to an extent of 8.75%.     

Synthesis,Characterization and Catalytic Activities of Palladium(II) Nitroaryl Complexes

Two palladium(II) nitroaryl complexes trans-[bromo(p-nitrophenyl)bis(triphenylphosphine)palladium(II)] 1 and trans-[bromo(2,4-dinitrophenyl)bis(triphenylphosphine)palladium(II)] 2 have been synthesized. The complexes were characterized by FTIR and NMR (¹H, ¹³C and ³¹P) spectroscopy and elemental analysis. The molecular structure of complex 2, as confirmed by X-ray crystallography, reveals that the Pd atom and its neighboring groups (two PPh₃, Br and phenylene group) lie in a slightly distorted square plane. In the UV–Vis spectra of the complexes 1 and 2, the palladium to aryl charge transfer bands were observed. The emission peaks from the singlet excited states (S₁ → S₀) were observed in the photoluminescence spectra of the complexes. The thermal stability of the complexes has been studied by thermal gravimetric analysis (TGA). TGA data showed that both complexes are thermally stable up to 200 °C, and complex 1 is more stable than 2. The catalytic efficiency of the new palladium(II) complexes was studied as demonstrated using the Sonogashira coupling reactions with good yields. The experimental results suggest that the Sonogashira coupling reactions can be performed at moderate temperature (50 °C) using these new palladium(II) complexes as catalysts.     

Tapered fiber bundles for combining high-power diode lasers

Tapered fiber bundles are often used to combine the output power of several semiconductor lasers into a multimode optical fiber for the purpose of pumping fiber lasers and amplifiers. It is generally recognized that the brightness of such combiners does not exceed the brightness of the individual input fibers. We report that the brightness of the tapered fibers (and fiber bundles) depends on both the taper ratio and the mode-filling properties of the beams launched into the individual fibers. Brightness, therefore, can be increased by selection of sources that fill a small fraction of the input fiber's modal capacity. As proof of concept, we present the results of measurements on tapered fiber-bundle combiners having a low-output étendue. Under low mode-filling conditions per input multimode fiber (i.e., fraction of filled modes < or =0.29), we report brightness enhancements of 8.0 dB for 19 x 1 bundles, 6.7 dB for 7 x 1 bundles, and 4.0 dB for 3 x 1 combiners. Our measured coupling efficiency variations of approximately 1%-2% among the various fibers in a given bundle confirm the uniformity and quality of the fabricated devices.