Development of a novel ANFIS-GA method for investigation of effect of inhibitors on asphaltene precipitation

Asphaltene precipitation and deposition in different parts of petroleum industry increase considerably cost and problems in oil production. Due to these facts controlling asphaltene precipitation becomes one of valuable topics for research in petroleum engineering. Utilization of Asphaltene inhibitors is known as one of the dominant methods for controlling asphaltene precipitation so in this paper Adaptive neuro-fuzzy inference system (ANFIS) is joint with Genetic Algorithm (GA) to study effectiveness of asphaltene inhibitors on precipitation in terms of oil and inhibitors properties. In order to prepare and evaluate the ANFIS-GA algorithm, some reliable experimental data were gathered. The obtained results from the comparison shows the coefficient of determination (R²) for training and testing phases are 0.98804 and 0.9916 respectively. The determined indexes and graphical comparisons expresses that ANFIS-GA has enough accuracy and potential to estimate effectiveness of inhibitors on asphaltene precipitation reductions.     

‘Breaking’ news: uncovering sense-breaking patterns in social media crisis communication during the 2017 Manchester bombing

ABSTRACT

Individuals, (media-) organisations, and crisis responders who are involved in ad hoc crisis communication steadily deploy social media to contribute to collective sense-making as an endeavour to create meaning in highly uncertain situations. Exerting sense-giving in order to shape others’ conceptions is causally preceded by an initial breakup of existing understanding. This study aims to explore patterns of sense-breaking in social media crisis communication and its impact on collective sense-making and sense-giving. To this end, we conducted a case study of the Manchester bombing in 2017, including a social network analysis of 708,147 Twitter postings and a content analysis of 2006 original tweets. We found individual role types to be initiators of sense-breaking in early crisis stages when uncertainty is at its height. Exerting successive sense-giving becomes more challenging if the collective sense-making has progressed along with the sequence of events. This understanding aims to encourage emergency management organisations to move their sense-giving actions closer to the point in time when sense-breaking occurs.     

Conducting Polymer Microcups for Organic Bioelectronics and Drug Delivery Applications

An ideal neural device enables long‐term, sensitive, and selective communication with the nervous system. To accomplish this task, the material interface should mimic the biophysical and the biochemical properties of neural tissue. By contrast, microfabricated neural probes utilize hard metallic conductors, which hinder their long‐term performance because these materials are not intrinsically similar to soft neural tissue. This study reports a method for the fabrication of monodisperse conducting polymer microcups. It is demonstrated that the physical surface properties of conducting polymer microcups can be precisely modulated to control electrical properties and drug‐loading/release characteristics.     

Covid-19 pandemic and the unprecedented mobilisation of scholarly efforts prompted by a health crisis: Scientometric comparisons across SARS,MERS and 2019-nCoV literature

Scientometrics - During the current century, each major coronavirus outbreak has triggered a quick and immediate surge of academic publications on its respective topic. The spike in research...     

Aggregation and clogging phenomena of rigid microparticles in microfluidics

We developed a numerical tool to investigate the phenomena of aggregation and clogging of rigid microparticles suspended in a Newtonian fluid transported through a straight microchannel. In a first step, we implement a time-dependent one-way coupling Discrete Element Method (DEM) technique to simulate the movement and effect of adhesion on rigid microparticles in two- and three-dimensional computational domains. The Johnson–Kendall–Roberts (JKR) theory of adhesion is applied to investigate the contact mechanics of particle–particle and particle–wall interactions. Using the one-way coupled solver, the agglomeration, aggregation and deposition behavior of the microparticles is studied by varying the Reynolds number and the particle adhesion. In a second step, we apply a two-way coupling CFD–DEM approach, which solves the equation of motion for each particle, and transfers the force field corresponding to particle–fluid interactions to the CFD toolbox OpenFOAM. Results for the one-way (DEM) and two-way (CFD–DEM) coupling techniques are compared in terms of aggregate size, aggregate percentages, spatial and temporal evaluation of aggregates in 2D and 3D. We conclude that two-way coupling is the more realistic approach, which can accurately capture the particle–fluid dynamics in microfluidic applications.     

Cross-VM cache attacks on Camellia

Journal of Computer Virology and Hacking Techniques - Flush+Reload is a powerful cache-based side-channel attack in which the attacker takes advantage of a security weakness in the X86 processor...     

Collaborating with Virtual Assistants in Organizations: Analyzing Social Loafing Tendencies and Responsibility Attribution

Information Systems Frontiers - Organizations increasingly introduce collaborative technologies in form of virtual assistants (VAs) to save valuable resources, especially when employees are...     

Building change detection after earthquake using multi-criteria decision analysis based on extracted information from high spatial resolution satellite images

Change detection of ground surface objects can provide essential and precious information for experts in the fields of Geomatics, emergency management, urban management, agriculture, and forestry. Space-borne remote-sensing images are one of the main sources for change detection. Various change detection methods have been proposed on remote-sensing applications. However, often, no single efficient method can be selected for a case study because the existing methods sometimes have good performance and sometimes perform poorly. Therefore, it is necessary to propose an integrated change detection method according to some change detection methods. Multi-criteria decision analysis is a powerful framework that can integrate several criteria that may be in contrast to each other. In this study, a multi-criteria decision analysis framework was used to integrate the spectral, textural, and transformed features for detecting building changes with the help of high spatial resolution satellite images. First, the spectral, textural, and transformed features were extracted from the pre- and post-event satellite images. Second, the spectral, textural, and transformed factor maps were produced by entering the related features to three separate Adaptive Network-Based Fuzzy Inference Systems (ANFIS). Third, the ANFIS model was used again to integrate the mentioned factor maps for producing the preliminary building change map. And finally, a comprehensive sensitivity analysis was carried out to determine the proper parameters of the ANFIS models leading to accurate change detection results. The proposed method was tested on the earthquake data set of Bam City in Iran. The achieved results indicated an overall accuracy of 89.62% for identifying the changed and unchanged building regions. Moreover, the obtained results proved the efficiency and accuracy of the proposed method with respect to other implemented methods regarding the Bam earthquake. Furthermore, the aggregation of the spectral, transformed, and textural features resulted in improving the change detection accuracy by about 5–15%, compared with the accuracy of every one of them for the mentioned purpose.     

A Taguchi analysis on structural properties of polypropylene microcellular nanocomposite foams containing Fe2O3 nanoparticles in batch process

Polypropylene (PP) as a thermoplastic polymer has been foamed using batch foaming process. CO₂ is used as the blowing agent of the foaming. Ferrous oxide nanoparticles (nano Fe₂O₃) are also added as reinforcement. Effect of different parameters including nanoparticle weight percentage, foaming temperature and time on the structural properties of PP/nano Fe₂O₃ nanocomposites is investigated using Taguchi approach. Scanning electron microscope results depict that an appropriate microcellular structure is obtained with the cell density of 10⁹ cells/cm³ and almost 1 μm of cell size. Analysis of variance results indicated that foaming temperature is the most significant parameter on the structural properties. Cell density and expansion ratio are decreased by increasing foaming temperature. This phenomenon could be due to the reducing melt strength of polymer/gas mixture. It was also inferred that adding 2 wt% of nanoparticles leads to 80% improvement in cell density while cell size and expansion ratio was decreased.