Effect of the adsorbate (Bromacil) equilibrium concentration in water on its adsorption on powdered activated carbon. Part 1. Equilibrium parameters

This study was carried out to investigate the adsorption equilibrium and kinetics of a pesticide of the uracil group on powdered activated carbon (PAC). The experiments were conducted at a wide range of initial pesticide concentrations (5 μg L⁻¹ to 500 μg L⁻¹ at pH 7.8), corresponding to equilibrium concentrations of less than 0.1 μg L⁻¹ for the weakest, which is compatible with the tolerance limits of drinking water. Such a very broad range of initial solute concentrations resulting powdered activated carbon (PAC) concentrations (0.1–5 mg L⁻¹) is the main particularity of our study. The application of several monosolute equilibrium models (two, three or more parameters) has generally shown that Bromacil adsorption is probably effective on two types of sites. High reactivity sites (K_L  10³ L mg⁻¹) which are 10–20 less present in a carbon surface than lower reactivity sites (K_L  10 L mg⁻¹), according to the q_m values calculated by two- or three-parameter models. The maximum capacity of the studied powdered activated carbon (PAC), corresponding to monolayer adsorption, compared to the Bromacil molecule surface, would be between 170 mg g⁻¹ and 190 mg g⁻¹. This theoretical value is very close to the experimental q_m values obtained when using linearized forms of Langmuir, Tóth and Fritz–Schluender models.     

Technology Landscape for Epidemiological Prediction and Diagnosis of COVID-19

The COVID-19 outbreak initiated from the Chinese city of Wuhan and eventually affected almost every nation around the globe. From China, the disease started spreading to the rest of the world. After China, Italy became the next epicentre of the virus and witnessed a very high death toll. Soon nations like the USA became severely hit by SARS-CoV-2 virus. The World Health Organisation, on 11th March 2020, declared COVID-19 a pandemic. To combat the epidemic, the nations from every corner of the world has instituted various policies like physical distancing, isolation of infected population and researching on the potential vaccine of SARS-CoV-2. To identify the impact of various policies implemented by the affected countries on the pandemic spread, a myriad of AI-based models have been presented to analyse and predict the epidemiological trends of COVID-19. In this work, the authors present a detailed study of different artificial intelligence frameworks applied for predictive analysis of COVID-19 patient record. The forecasting models acquire information from records to detect the pandemic spreading and thus enabling an opportunity to take immediate actions to reduce the spread of the virus. This paper addresses the research issues and corresponding solutions associated with the prediction and detection of infectious diseases like COVID-19. It further focuses on the study of vaccinations to cope with the pandemic. Finally, the research challenges in terms of data availability, reliability, the accuracy of the existing prediction models and other open issues are discussed to outline the future course of this study.     

Probing a Device's Active Atoms

Materials science and device studies have, when implemented jointly as “operando” studies, better revealed the causal link between the properties of the device's materials and its operation, with applications ranging from gas sensing to information and energy technologies. Here, as a further step that maximizes this causal link, the paper focuses on the electronic properties of those atoms that drive a device's operation by using it to read out the materials property. It is demonstrated how this method can reveal insight into the operation of a macroscale, industrial‐grade microelectronic device on the atomic level. A magnetic tunnel junction's (MTJ's) current, which involves charge transport across different atomic species and interfaces, is measured while these atoms absorb soft X‐rays with synchrotron‐grade brilliance. X‐ray absorption is found to affect magnetotransport when the photon energy and linear polarization are tuned to excite Fe? O bonds parallel to the MTJ's interfaces. This explicit link between the device's spintronic performance and these Fe? O bonds, although predicted, challenges conventional wisdom on their detrimental spintronic impact. The technique opens interdisciplinary possibilities to directly probe the role of different atomic species on device operation, and shall considerably simplify the materials science iterations within device research.     

<Emphasis Type="Italic">In Situ</Emphasis> Local Measurement of Austenite Mechanical Stability and Transformation Behavior in Third-Generation Advanced High-Strength Steels

Austenite mechanical stability, i.e., retained austenite volume fraction (RAVF) variation with strain, and transformation behavior were investigated for two third-generation advanced high-strength steels (3GAHSS) under quasi-static uniaxial tension: a 1200 grade, two-phase medium Mn (10 wt pct) TRIP steel, and a 980 grade, three-phase TRIP steel produced with a quenching and partitioning heat treatment. The medium Mn (10 wt pct) TRIP steel deforms inhomogeneously via propagative instabilities (Lüders and Portevin Le Châtelier-like bands), while the 980 grade TRIP steel deforms homogenously up to necking. The dramatically different deformation behaviors of these steels required the development of a new in situ experimental technique that couples volumetric synchrotron X-ray diffraction measurement of RAVF with surface strain measurement using stereo digital image correlation over the beam impingement area. Measurement results with the new technique are compared to those from a more conventional approach wherein strains are measured over the entire gage region, while RAVF measurement is the same as that in the new technique. A determination is made as to the appropriateness of the different measurement techniques in measuring the transformation behaviors for steels with homogeneous and inhomogeneous deformation behaviors. Extension of the new in situ technique to the measurement of austenite transformation under different deformation modes and to higher strain rates is discussed.     

Fatigue behavior of RC beams strengthened with prestressed NSM CFRP rods

The fatigue performance of Reinforced Concrete (RC) beams strengthened using NSM CFRP rods was examined in this study. The testing matrix consisted of one un-strengthened beam and four beams strengthened using NSM CFRP rods prestressed to effective strain values of 0, 3260, 6899, and 9177 μ representing 0%, 20.4%, 43.1%, and 57.4% of the CFRP rod ultimate tensile strain, 1.6%. All beams were tested in four-point bending under fatigue conditions representing in-service loading for 3 million cycles at a frequency of 2 Hz. Upper and lower load limits were chosen to induce a stress range of 125 MPa in the tension steel during the first cycle. The fatigue results were compared with experimental test results of identical beams strengthened using prestressed NSM CFRP strips tested under identical fatigue conditions found elsewhere in literature. Test results showed that all strengthened beams experienced deflection increase lower than that of the un-strengthened beam which indicates the efficiency of the strengthening process in reducing the damage accumulation. Also, the percentage deflection increase as well as the stiffness degradation after 3 million cycles are almost the same for all the strengthened beams which indicates that damage accumulation is independent from the prestress level. The groove dimensionality, rather than the CFRP geometry, has a detrimental effect on the bond behavior.     

Finite Element Dynamic Analysis of Laminated Viscoelastic Structures

This work is concerned with the dynamic behavior of laminated beam, plate and shell structures consisting of a viscoelastic damping layer constrained between two structural layers. Finite element models for modal, harmonic and transient analyses are developed. The dynamic interlaminar shear stresses are determined and presented under harmonic and transient loads. The effect of the damping ratio of the viscoelastic material is investigated. It is found that the viscoelastic material damping reduces the interlaminar stresses. The results also show the dependency of the viscoelastic material on frequency, hence, the effect of the viscoelastic material appears significantly under harmonic loading. In transient analysis, the importance of the viscoelastic material is observed in absorbing the impact and returning the structure to its original configuration.     

User-centric hybrid semi-autoencoder recommendation system

Recommendation System is one of such solutions to overcome information overload issues and to identify products most relevant to users and provide suggestions to users for items they might be interested in consuming or elements matching their needs. The significant challenge of several recommendation approaches is that they suggested a huge number of things to the target user. But the exciting items, according to the target user, are seen at the bottom of the recommended list. The proposed approach has improved the quality of recommendations by implementing some of the unique features in the new framework of auto encoder called semi-autoencoder, which contains the rating information as well as some additional information of users. Autoencoder is widely used in the recommender system because it gives the best result for feature extraction, dimensionality reduction, regeneration of data, and a better understanding of the user’s characteristics. The experimental results are compared with some established popular methods using precision, recall, and F-measure evaluation measures. Users generally don’t want to see lots of suggestions. With its six building blocks, the proposed approach gives better performance for the top 10 recommendations compared to other well-known methods.

     

Efficient deployment of wireless sensor networks targeting environment monitoring applications

Maximizing network connectivity while maintaining a useful lifetime period without exceeding cost constraints is a challenging design objective for wireless sensor networks. Satisfying such objective becomes even a more intricate task with 3-D setups and harsh operational conditions found in typical large scale environment monitoring applications. While much work has been performed in environment monitoring, only few have addressed the unique characteristics of such applications. In this paper, we introduce a novel 3-D deployment strategy, called Optimized 3-D deployment with Lifetime Constraint (O3DwLC), for relay nodes in environmental applications. The strategy optimizes network connectivity, while guarantying specific network lifetime and limited cost. Key to our contribution is a very limited search space for the optimization problem, in addition to a revised definition for network lifetime that is more appropriate in environment monitoring. The effectiveness of our strategy is validated through extensive simulations and comparisons, assuming practical considerations of signal propagation and connectivity.     

Facial expression recognition using tracked facial actions: Classifier performance analysis

In this paper, we address the analysis and recognition of facial expressions in continuous videos. More precisely, we study classifiers performance that exploit head pose independent temporal facial action parameters. These are provided by an appearance-based 3D face tracker that simultaneously provides the 3D head pose and facial actions. The use of such tracker makes the recognition pose- and texture-independent. Two different schemes are studied. The first scheme adopts a dynamic time warping technique for recognizing expressions where training data are given by temporal signatures associated with different universal facial expressions. The second scheme models temporal signatures associated with facial actions with fixed length feature vectors (observations), and uses some machine learning algorithms in order to recognize the displayed expression. Experiments quantified the performance of different schemes. These were carried out on CMU video sequences and home-made video sequences. The results show that the use of dimension reduction techniques on the extracted time series can improve the classification performance. Moreover, these experiments show that the best recognition rate can be above 90%.     

A conceptual framework for blockchain smart contract adoption to manage real estate deals in smart cities

Neural Computing and Applications - Blockchains-based smart contracts are disrupting the smart real estate sector of the smart cities. The current study explores the literature focused on...