Search results for: domain decomposition

Authors: Felix Baumann, Aleksandar Milutinovic, Dieter Roller

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Up to this point business process management projects in general and business process modelling projects in particular could not rely on a practical and scientifically validated method to estimate cost and effort. Especially the model development phase is not covered by a cost estimation method or model. Further phases of business process modelling starting with implementation are covered by initial solutions which are discussed in the literature. This article proposes a method of filling this gap by deriving a cost estimation method from available methods in similar domains namely software development or software engineering. Software development is regarded as closely similar to process modelling as we show. After the proposition of this method different ideas for further analysis and validation of the method are proposed. We derive this method from COCOMO II and Function Point which are established methods of effort estimation in the domain of software development. For this we lay out similarities of the software development rocess and the process of process modelling which is a phase of the Business Process Management life-cycle.

Keywords: COCOMO II, busines process modeling, cost estimation method, BPM COCOMO

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Authors: Mikkel Snorre Wilms Boysen

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From a theoretical perspective, expertise is generally considered a precondition for creativity. The assumption is that an individual needs to master the common and accepted rules and techniques within a certain knowledge-domain in order to create something new and valuable. However, real life cases, and a limited amount of empirical studies, demonstrate that this assumption may be overly simple. In this article, this question is explored through a number of semi-experimental case studies conducted within the fields of music, technology, and youth culture. The studies indicate that, in various ways, expertise plays an important part in creative processes. However, the case studies also indicate that expertise sometimes leads to an entrenched perspective, in the sense that knowledge and experience may work as a path into the well-known rather than into the unknown. In this article, these issues are explored with reference to different theoretical approaches to creativity and learning, including actor-network theory, the theory of blind variation and selective retention, and Csikszentmihalyi’s system model. Finally, some educational aspects and implications of this are discussed.

Keywords: creativity, expertise , education, technology

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Authors: Sandesh Achar

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Artificial intelligence is being increasingly incorporated into many applications across various sectors such as health, education, security, and agriculture. Recently, there has been rapid development in cloud computing technology, resulting in AI’s implementation into cloud computing to enhance and optimize the technology service rendered. The deployment of AI in cloud-based applications has brought about autonomous computing, whereby systems achieve stated results without human intervention. Despite the amount of research into autonomous computing, work incorporating AI/ML into cloud computing to enhance its performance and resource allocation remain a fundamental challenge. This paper highlights different manifestations, roles, trends, and challenges related to AI-based cloud computing models. This work reviews and highlights excellent investigations and progress in the domain. Future directions are suggested for leveraging AI/ML in next-generation computing for emerging computing paradigms such as cloud environments. Adopting AI-based algorithms and techniques to increase operational efficiency, cost savings, automation, reducing energy consumption and solving complex cloud computing issues are the major findings outlined in this paper.

Keywords: artificial intelligence, cloud computing, deep learning, machine learning, internet of things

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Authors: Solaine Hachem, Frédéric Bourquin, Dominique Siegert

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The brutal collapse of bridges is mainly due to scour. Indeed, the soil erosion in the riverbed around a pier modifies the embedding conditions of the structure, reduces its overall stiffness and threatens its stability. Hence, finding an efficient technique that allows early scour detection becomes mandatory. Vibration analysis is an indirect method for scour detection that relies on real-time monitoring of the bridge. It tends to indicate the presence of a scour based on its consequences on the stability of the structure and its dynamic response. Most of the research in this field has focused on the dynamic behavior of a single pile and has examined the depth of the scour. In this paper, a bridge is fully modeled with all piles and spans and the scour is represented by a reduction in the foundation's stiffnesses. This work aims to identify the vibration modes sensitive to the rigidity’s loss in the foundations so that their variations can be considered as a scour indicator: the decrease in soil-structure interaction rigidity leads to a decrease in the natural frequencies’ values. By using the first-order perturbation method, the expression of sensitivity, which depends only on the selected vibration modes, is established to determine the deficiency of foundations stiffnesses. The solutions are obtained by using the singular value decomposition method for the regularization of the inverse problem. The propagation of uncertainties is also calculated to verify the efficiency of the inverse problem method. Numerical simulations describing different scenarios of scour are investigated on a simplified model of a real composite steel-concrete bridge located in France. The results of the modal analysis show that the modes corresponding to in-plane and out-of-plane piers vibrations are sensitive to the loss of foundation stiffness. While the deck bending modes are not affected by this damage.

Keywords: bridge’s piers, inverse problems, modal sensitivity, scour detection, vibration analysis

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Authors: Andrew Laming, John Hattie, Mark Wilson

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Nine Queensland Independent high schools provided deidentified student-matched ATAR and NAPLAN data for all 1217 ATAR graduates since 2020 who also sat NAPLAN at the school. Graduating cohorts from the nine schools contained a mean 100 ATAR graduates with previous NAPLAN data from their school. Excluded were vocational students (mean=27) and any ATAR graduates without NAPLAN data (mean=20). Based on Index of Community Socio-Educational Access (ICSEA) prediction, all schools had larger that predicted proportions of their students graduating with ATARs. There were an additional 173 students not releasing their ATARs to their school (14%), requiring this data to be inferred by schools. Gain was established by first converting each student’s strongest NAPLAN domain to a statewide percentile, then subtracting this result from final ATAR. The resulting ‘percentile shift’ was corrected for plausible ATAR participation at each NAPLAN level. Strongest NAPLAN domain had the highest correlation with ATAR (R2=0.58). RESULTS School mean NAPLAN scores fitted ICSEA closely (R2=0.97). Schools achieved a mean cohort gain of two ATAR rankings, but only 66% of students gained. This ranged from 46% of top-NAPLAN decile students gaining, rising to 75% achieving gains outside the top decile. The 54% of top-decile students whose ATAR fell short of prediction lost a mean 4.0 percentiles (or 6.2 percentiles prior to correction for regression to the mean). 71% of students in smaller schools gained, compared to 63% in larger schools. NAPLAN variability in each of the 13 ICSEA1100 cohorts was 17%, with both intra-school and inter-school variation of these values extremely low (0.3% to 1.8%). Mean ATAR change between years in each school was just 1.1 ATAR ranks. This suggests consecutive school cohorts and ICSEA-similar schools share very similar distributions and outcomes over time. Quantile analysis of the NAPLAN/ATAR revealed heteroscedasticity, but splines offered little additional benefit over simple linear regression. The NAPLAN/ATAR R2 was 0.33. DISCUSSION Standardised data like NAPLAN and ATAR offer educators a simple no-cost progression metric to analyse performance in conjunction with their internal test results. Change is expressed in percentiles, or ATAR shift per student, which is layperson intuitive. Findings may also reduce ATAR/vocational stream mismatch, reveal proportions of cohorts meeting or falling short of expectation and demonstrate by how much. Finally, ‘crashed’ ATARs well below expectation are revealed, which schools can reasonably work to minimise. The percentile shift method is neither value-add nor a growth percentile. In the absence of exit NAPLAN testing, this metric is unable to discriminate academic gain from legitimate ATAR-maximizing strategies. But by controlling for ICSEA, ATAR proportion variation and student mobility, it uncovers progression to ATAR metrics which are not currently publicly available. However achieved, ATAR maximisation is a sought-after private good. So long as standardised nationwide data is available, this analysis offers useful analytics for educators and reasonable predictivity when counselling subsequent cohorts about their ATAR prospects.  

Keywords: NAPLAN, ATAR, analytics, measurement, gain, performance, data, percentile, value-added, high school, numeracy, reading comprehension, variability, regression to the mean

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Authors: Dassou Nagassou

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Recent increase in frequency and severity of climatic impacts throughout the world has put a particular emphasis on the urgency to address the anthropogenic greenhouse gas emissions. The latter, mainly composed of carbon dioxide are responsible for the global warming of planet earth. Despite efforts to transition towards a zero-emission economy, manufacturing industries, electricity generation power plants, and transportation sectors continue to encounter challenges which hinder their progress towards a full decarbonization. The growing energy demand from both developed and under-developed economies exacerbates the situation and as a result, more carbon dioxide is discharged into the atmosphere. This situation imposes a lot of constraints on industries which are involved i.e., manufacturing industries, transportation, and electricity generation which must navigate the stringent environmental regulations in order to remain profitable. Existing solutions such as energy efficiencies, green materials (life cycle analysis), and many more have fallen short to address the problem due to their inadaptation to existing infrastructures, low efficiencies, and prohibitive costs. The proposed technology exploits the synergistic interaction between solar radiation and plasma to boost a direct decomposition of the molecules of carbon dioxide while producing alternative fuels which can be used to sustain on-site high-temperature processes via 100% solar energy harvesting in the form of photons and electricity. The advantages of this technology and its ability to be easily integrated into existing systems make it appealing for the industry which can now afford to fast track on the path towards full decarbonization, thanks to the solar plasma reactor. Despite the promising experimental results which proved the viability of this concept, solar-plasma reactors require further investigations to understand the synergistic interactions between plasma and solar radiation for a potential technology scale-up.

Keywords: solar, non-equilibrium, plasma, reactor, greenhouse-gases, solar-fuels

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Authors: N. Fusun Oyman Serteller

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In this paper, the techniques to solve time dependent electromagnetic wave propagation equations based on the Finite Difference Method (FDM) are proposed by comparing the results with Finite Element Method (FEM) in 2D while discussing some special simulation examples.  Here, 2D dynamical wave equations for lossy media, even with a constant source, are discussed for establishing symbolic manipulation of wave propagation problems. The main objective of this contribution is to introduce a comparative study of two suitable numerical methods and to show that both methods can be applied effectively and efficiently to all types of wave propagation problems, both linear and nonlinear cases, by using symbolic computation. However, the results show that the FDM is more appropriate for solving the nonlinear cases in the symbolic solution. Furthermore, some specific complex domain examples of the comparison of electromagnetic waves equations are considered. Calculations are performed through Mathematica software by making some useful contribution to the programme and leveraging symbolic evaluations of FEM and FDM.

Keywords: finite difference method, finite element method, linear-nonlinear PDEs, symbolic computation, wave propagation equations

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Authors: Jamid Ul Islam, Zillur Rahman

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The impulse for customer engagement research in online brand communities (OBCs) is largely acknowledged in the literature. Applying congruity theory, this study proposes a model of customer engagement by examining how two congruities viz. self-brand image congruity and value congruity influence customers’ engagement in online brand communities. The consequent effect of customer engagement on brand loyalty is also studied. This study collected data through a questionnaire survey of 395 students of a higher educational institute in India, who were active on Facebook and followed a brand community (at least one). The data were analyzed using structure equation modelling. The results revealed that both the types of congruity i.e., self-brand image congruity and value congruity significantly affect customer engagement. A positive effect of customer engagement on brand loyalty was also affirmed by the results. This study integrates and broadens extant explanations of different congruity effects on consumer behavior-an area that has received little attention. This study is expected to add new trends to engage customers in online brand communities and offer realistic insights to the domain of social media marketing.

Keywords: congruity theory, customer engagement, Facebook, online brand communities

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Authors: Havish Nalapareddy, Mark V. Albert, Ranak Bansal, Avi Udash, Lin Lin

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Students engage in many course projects during their degree programs. However, impactful projects often need a time frame longer than a single semester. Ideally, projects are documented and structured to be readily accessible to future students who may choose to continue the project, with features that emphasize the local community, university, or course structure. The Research and Project Portal (RAPP) is a place where students can post both their completed and ongoing projects with all the resources and tools used. This portal allows students to see what other students have done in the past, in the same university environment, related to their domain of interest. Computer science instructors or students selecting projects can use this portal to assign or choose an incomplete project. Additionally, this portal allows non-computer science faculty and industry collaborators to document their project ideas for students in courses to prototype directly, rather than directly soliciting the help of instructors in engaging students. RAPP serves as a platform linking students across classes and faculty both in and out of computer science courses on joint projects to encourage long-term project efforts across semesters or years.

Keywords: education, technology, research, academic portal

Procedia PDF Downloads 138

Authors: M. S. Jouini, A. Al-Sumaiti, M. Tembely, K. Rahimov

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Characterizing rock properties of carbonate reservoirs is highly challenging because of rock heterogeneities revealed at several length scales. In the last two decades, the Digital Rock Physics (DRP) approach was implemented successfully in sandstone rocks reservoirs in order to understand rock properties behaviour at the pore scale. This approach uses 3D X-ray Microtomography images to characterize pore network and also simulate rock properties from these images. Even though, DRP is able to predict realistic rock properties results in sandstone reservoirs it is still suffering from a lack of clear workflow in carbonate rocks. The main challenge is the integration of properties simulated at different scales in order to obtain the effective rock property of core plugs. In this paper, we propose several approaches to characterize absolute permeability in some carbonate core plugs samples using multi-scale numerical simulation workflow. In this study, we propose a procedure to simulate porosity and absolute permeability of a carbonate rock sample using textures of Micro-Computed Tomography images. First, we discretize X-Ray Micro-CT image into a regular grid. Then, we use a textural parametric model to classify each cell of the grid using supervised classification. The main parameters are first and second order statistics such as mean, variance, range and autocorrelations computed from sub-bands obtained after wavelet decomposition. Furthermore, we fill permeability property in each cell using two strategies based on numerical simulation values obtained locally on subsets. Finally, we simulate numerically the effective permeability using Darcy’s law simulator. Results obtained for studied carbonate sample shows good agreement with the experimental property.

Keywords: multiscale modeling, permeability, texture, micro-tomography images

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Authors: Nazish Saleem Abbas, Muhammad Haris Jamil, Hamid Sharif

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Digital watermarking is a technique that allows an individual to add and hide secret information, copyright notice, or other verification message inside a digital audio, video, or image. Today, with the advancement of technology, modern healthcare systems manage patients’ diagnostic information in a digital way in many countries. When transmitted between hospitals through the internet, the medical data becomes vulnerable to attacks and requires security and confidentiality. Digital watermarking techniques are used in order to ensure the authenticity, security and management of medical images and related information. This paper proposes a watermarking technique that embeds a watermark in medical images imperceptibly and securely. In this work, digital watermarking on medical images is carried out using the Least Significant Bit (LSB) with the Discrete Cosine Transform (DCT). The proposed methods of embedding and extraction of a watermark in a watermarked image are performed in the frequency domain using LSB by XOR operation. The quality of the watermarked medical image is measured by the Peak signal-to-noise ratio (PSNR). It was observed that the watermarked medical image obtained performing XOR operation between DCT and LSB survived compression attack having a PSNR up to 38.98.

Keywords: watermarking, image processing, DCT, LSB, PSNR

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Authors: Thilina Ranaweera, Enes Makalic, John L. Hopper, Adrian Bickerstaffe

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Data are the primary asset of biomedical researchers, and the engine for both discovery and research translation. As the volume and complexity of research datasets increase, especially with new technologies such as large single nucleotide polymorphism (SNP) chips, so too does the requirement for software to manage, process and analyze the data. Researchers often need to execute complicated queries and conduct complex analyzes of large-scale datasets. Existing tools to analyze such data, and other types of high-dimensional data, unfortunately suffer from one or more major problems. They typically require a high level of computing expertise, are too simplistic (i.e., do not fit realistic models that allow for complex interactions), are limited by computing power, do not exploit the computing power of large-scale parallel architectures (e.g. supercomputers, GPU clusters etc.), or are limited in the types of analysis available, compounded by the fact that integrating new analysis methods is not straightforward. Solutions to these problems, such as those developed and implemented on parallel architectures, are currently available to only a relatively small portion of medical researchers with access and know-how. The past decade has seen a rapid expansion of data management systems for the medical domain. Much attention has been given to systems that manage phenotype datasets generated by medical studies. The introduction of heterogeneous genomic data for research subjects that reside in these systems has highlighted the need for substantial improvements in software architecture. To address this problem, we have developed SPARK, an enabling and translational system for medical research, leveraging existing high performance computing resources, and analysis techniques currently available or being developed. It builds these into The Ark, an open-source web-based system designed to manage medical data. SPARK provides a next-generation biomedical data management solution that is based upon a novel Micro-Service architecture and Big Data technologies. The system serves to demonstrate the applicability of Micro-Service architectures for the development of high performance computing applications. When applied to high-dimensional medical datasets such as genomic data, relational data management approaches with normalized data structures suffer from unfeasibly high execution times for basic operations such as insert (i.e. importing a GWAS dataset) and the queries that are typical of the genomics research domain. SPARK resolves these problems by incorporating non-relational NoSQL databases that have been driven by the emergence of Big Data. SPARK provides researchers across the world with user-friendly access to state-of-the-art data management and analysis tools while eliminating the need for high-level informatics and programming skills. The system will benefit health and medical research by eliminating the burden of large-scale data management, querying, cleaning, and analysis. SPARK represents a major advancement in genome research technologies, vastly reducing the burden of working with genomic datasets, and enabling cutting edge analysis approaches that have previously been out of reach for many medical researchers.

Keywords: biomedical research, genomics, information systems, software

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Authors: Muthukumarasamy Govindarajan

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Detection of unwanted, unsolicited mails called spam from email is an interesting area of research. It is necessary to evaluate the performance of any new spam classifier using standard data sets. Recently, ensemble-based classifiers have gained popularity in this domain. In this research work, an efficient email filtering approach based on ensemble methods is addressed for developing an accurate and sensitive spam classifier. The proposed approach employs Naive Bayes (NB), Support Vector Machine (SVM) and Genetic Algorithm (GA) as base classifiers along with different ensemble methods. The experimental results show that the ensemble classifier was performing with accuracy greater than individual classifiers, and also hybrid model results are found to be better than the combined models for the e-mail dataset. The proposed ensemble-based classifiers turn out to be good in terms of classification accuracy, which is considered to be an important criterion for building a robust spam classifier.

Keywords: accuracy, arcing, bagging, genetic algorithm, Naive Bayes, sentiment mining, support vector machine

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Authors: Sunghae Jun

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Technology management (TM) is important issue in a company improving the competitiveness. Among many activities of TM, technology analysis (TA) is important factor, because most decisions for management of technology are decided by the results of TA. TA is to analyze the developed results of target technology using statistics or Delphi. TA based on Delphi is depended on the experts’ domain knowledge, in comparison, TA by statistics and machine learning algorithms use objective data such as patent or paper instead of the experts’ knowledge. Many quantitative TA methods based on statistics and machine learning have been studied, and these have been used for technology forecasting, technological innovation, and management of technology. They applied diverse computing tools and many analytical methods case by case. It is not easy to select the suitable software and statistical method for given TA work. So, in this paper, we propose a methodology for quantitative TA using statistical computing software called R and data science to construct a general framework of TA. From the result of case study, we also show how our methodology is applied to real field. This research contributes to R&D planning and technology valuation in TM areas.

Keywords: technology management, R system, R data science, statistics, machine learning

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Authors: Zayneb Kthiri, Walid Hamada

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The application of organic fertilizers is generally known to be useful to sustain soil fertility and plant growth, especially in poor soils, with less than 1% of organic matter, as it is very common in our Tunisian fields. Therefore, we focused on evaluating the effect of a new released liquid organic fertilizer named Solorga (with 5% of organic matter) compared to a reference product (Espartan: Kimitec, Spain) on tomato plant growth and physiology. Both fertilizers, derived from plant decomposition, were applied at an early stage in hydroponic system and under greenhouse. In hydroponic system, after 14 days of their application by root feeding, a significant difference was observed between treatments. Indeed, Solorga improved shoots and roots length, as well as the biomass respectively, by 45%, 27%, and 27.8% increase rate, while compared to control plants. However, Espartan induced less the measured parameters while compared to untreated control. Moreover, Solorga significantly increased the chlorophyll content by 42% compared to control and by 32% compared to Espartan. In the greenhouse, after 20 days of treatments, the results showed a significant effect of both fertilizers on SPAD index and the number of flowers blossom. Solorga increased the amount of chlorophyll present in the leaf by 7% compared to Espartan as well as the plant height under greenhouse. Moreover, the number of flowers blossom increased by 15% in plants treated with Solorga while compared to Espartan. Whereas, there is no notable difference between both organic fertilizers on the fruits blossom and the number of fruits per blossom. In conclusion, even though there is a difference in the organic matter between both fertilizers, Solorga improved better the plant growth in controlled conditions in hydroponic system while compared to Espartan. Altogether the obtained results are encouraging for the use of Solorga as a soil enriching source of organic matter to help plants to boost their growth and help them to overcome abiotic stresses linked to soil fertility.

Keywords: tomato, plant growth, organic fertilizer, hydroponic system, greenhouse

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Authors: Armel Asongu Nkembi, Ahmad Fawad

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The aim of this paper is to determine in a concise way the transfer function that characterizes a DC electrical motor with a helix. In practice it can be obtained by applying a particular input to the system and then, based on the observation of its output, determine an approximation to the transfer function of the system. In our case, we use a step input and find the transfer function parameters that give the simulated first-order time response. The simulation of the system is done using MATLAB/Simulink. In order to determine the parameters, we assume a first order system and use the Broida approximation to determine the parameters and then its Mean Square Error (MSE). Furthermore, we design a PID controller for the control process first in the continuous time domain and tune it using the Ziegler-Nichols open loop process. We then digitize the controller to obtain a digital controller since most systems are implemented using computers, which are digital in nature.

Keywords: transfer function, step input, MATLAB, Simulink, DC electrical motor, PID controller, open-loop process, mean square process, digital controller, Ziegler-Nichols

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Authors: Sanjay Singh, Sushil Kumar, Rashmi Jain

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Quantum registering is another pattern in computational hypothesis and a quantum mechanical framework has a few helpful properties like Entanglement. We plan to store data concerning the structure and substance of a basic picture in a quantum framework. Consider a variety of n qubits which we propose to use as our memory stockpiling. In recent years classical processing is switched to quantum image processing. Quantum image processing is an elegant approach to overcome the problems of its classical counter parts. Image storage, retrieval and its processing on quantum machines is an emerging area. Although quantum machines do not exist in physical reality but theoretical algorithms developed based on quantum entangled states gives new insights to process the classical images in quantum domain. Here in the present work, we give the brief overview, such that how entangled states can be useful for quantum image storage and retrieval. We discuss the properties of tripartite Greenberger-Horne-Zeilinger and W states and their usefulness to store the shapes which may consist three vertices. We also propose the techniques to store shapes having more than three vertices.

Keywords: Greenberger-Horne-Zeilinger, image storage and retrieval, quantum entanglement, W states

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Authors: Dahi Ghareab Abdelsalam Ibrahim

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Calibration of step heights with high accuracy is needed for many applications in the industry. In general, step height consists of three bands: pass band, transition band (roll-off), and stop band. Abdelsalam used a convolution of the transfer functions of both Chebyshev type 2 and elliptic filters with WFF of the Fresnel transform in the frequency domain for producing a steeper roll-off with the removal of ripples in the pass band- and stop-bands. In this paper, we used a new method based on the Hamming window: band-pass filter for calibration of step heights in terms of perfect adjustment of pass-band, roll-off, and stop-band. The method is applied to calibrate a nominal step height of 40 cm. The step height is measured first by asynchronous dual-wavelength phase-shift interferometry. The measured step height is then calibrated by the simulation of the Hamming window: band-pass filter. The spectrum of the simulated band-pass filter is simulated at N = 881 and f0 = 0.24. We can conclude that the proposed method can calibrate any step height by adjusting only two factors which are N and f0.

Keywords: optical metrology, step heights, hamming window, band-pass filter

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Authors: W. Stawiński, A. Wegrzyn, O. M. Freitas, S. A. Figueiredo

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Water is a life supporting resource, crucial for humanity and essential for natural ecosystems, which have been endangered by developing industry and increasing human population. Dyes are common in effluents discharged by various industries such as paper, plastics, food, cosmetics, and textile. They produce toxic effects on animals and disturb natural biological processes in receiving waters. Having complex molecular structure and resistance to biological decomposition they are problematic and difficult to be treated by conventional methods. In the search of efficient and sustainable method, sorption has been getting more interest in application to wastewaters treatment. Clays are minerals that have a layer structure based on phyllosilicate sheets that may carry a charge, which is balanced by ions located between the sheets. These charge-balancing ions can be exchanged resulting in very good ion-exchange properties of the material. Modifications of clays enhance their properties, producing a good and inexpensive sorbent for the removal of pollutants from wastewaters. The presented work proves that the treatment of a clay, vermiculite, with nitric acid followed by washing in citric acid strongly increases the sorption of two cationic dyes, methylene blue (C.I. 52015) and astrazon red (C.I. 110825). Desorption studies showed that the best eluent for regeneration is a solution of NaCl in ethanol. Cycles of sorption and desorption in column system showed no significant deterioration of sorption capacity and proved that the material shows a very good performance as sorbent, which can be recycled and reused. The results obtained open new possibilities of further modifications on vermiculite and modifications of other materials in order to get very efficient sorbents useful for wastewater treatment.

Keywords: cationic dyestuffs, sorption and regeneration, vermiculite, wastewater treatment

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Authors: Wissem Tighidet, Faïçal Naït Bouda, Moussa Allouche

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The numerical study of the Fluid-Structure Interaction (FSI) in a partially filled flexible tank submitted to a horizontal harmonic excitation motion. It is investigated by using two-way Fluid-Structure Interaction (FSI) in a flexible tank by Coupling between the Transient Structural (Mechanical) and Fluid Flow (Fluent) in ANSYS-Workbench Student version. The Arbitrary Lagrangian-Eulerian (ALE) formulation is adopted to solve with the finite volume method, the Navier-Stokes equations in two phases in a moving domain. The Volume of Fluid (VOF) method is applied to track the free surface. However, the equations of the dynamics of the structure are solved with the finite element method assuming a linear elastic behavior. To conclude, the Fluid-Structure Interaction (IFS) has a vital role in the analysis of the dynamic behavior of the rectangular tank. The results indicate that the flexibility of the tank walls has a significant impact on the amplitude of tank sloshing and the deformation of the free surface as well as the effect of liquid sloshing on wall deformation.

Keywords: arbitrary lagrangian-eulerian, fluid-structure interaction, sloshing, volume of fluid

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Authors: Scott N. Gerard, Aliza Heching, Susann M. Keohane, Samuel S. Adams

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The world-wide population of people over 60 years of age is growing rapidly. The explosion is placing increasingly onerous demands on individual families, multiple industries and entire countries. Current, human-intensive approaches to eldercare are not sustainable, but IoT and AI technologies can help. The Knowledge Reactor (KR) is a contextual, data fusion engine built to address this and other similar problems. It fuses and centralizes IoT and System of Record/Engagement data into a reactive knowledge graph. Cognitive applications and services are constructed with its multiagent architecture. The KR can scale-up and scaledown, because it exploits container-based, horizontally scalable services for graph store (JanusGraph) and pub-sub (Kafka) technologies. While the KR can be applied to many domains that require IoT and AI technologies, this paper describes how the KR specifically supports the challenging domain of cognitive eldercare. Rule- and machine learning-based analytics infer activities of daily living from IoT sensor readings. KR scalability, adaptability, flexibility and usability are demonstrated.

Keywords: ambient sensing, AI, artificial intelligence, eldercare, IoT, internet of things, knowledge graph

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Authors: Manar Amayri, Hussain Kazimi, Quoc-Dung Ngo, Stephane Ploix

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A general approach is proposed to determine occupant behavior (occupancy and activity) in residential buildings and to use these estimates for improved energy management. Occupant behaviour is modelled with a Bayesian Network in an unsupervised manner. This algorithm makes use of domain knowledge gathered via questionnaires and recorded sensor data for motion detection, power, and hot water consumption as well as indoor CO₂ concentration. Two case studies are presented which show the real world applicability of estimating occupant behaviour in this way. Furthermore, experiments integrating occupancy estimation and hot water production control show that energy efficiency can be increased by roughly 5% over known optimal control techniques and more than 25% over rule-based control while maintaining the same occupant comfort standards. The efficiency gains are strongly correlated with occupant behaviour and accuracy of the occupancy estimates.

Keywords: energy, management, control, optimization, Bayesian methods, learning theory, sensor networks, knowledge modelling and knowledge based systems, artificial intelligence, buildings

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Authors: Yazid Alkraimeen

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Precise prediction of dielectric stresses and high voltages of power transformers require the accurate calculation of frequency-dependent parameters. A lack of accuracy can result in severe damages to transformer windings. Transient conditions is stuided by digital computers, which require the implementation of accurate models. This paper analyzes the computation of frequency-dependent skin and proximity losses included in the transformer winding model, using analytical equations and Finite Element Method (FEM). A modified formula to calculate the proximity and the skin losses is presented. The results of the frequency-dependent parameter calculations are verified using the Finite Element Method. The time-domain transient voltages are obtained using Numerical Inverse Laplace Transform. The results show that the classical formula for proximity losses is overestimating the transient voltages when compared with the results obtained from the modified method on a simple transformer geometry.

Keywords: fast front transients, proximity losses, transformer winding modeling, skin losses

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Authors: Sameh Baccari, Mahmoud Neji

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In recent years, the rapid developments on mobile devices and wireless technologies enable new dimension capabilities for the learning domain. This dimension facilitates people daily activities and shortens the distances between individuals. When these technologies have been used in learning, a new paradigm has been emerged giving birth to mobile learning. Because of the mobility feature, m-learning courses have to be adapted dynamically to the learner’s context. The main challenge in context-aware mobile learning is to develop an approach building the best learning resources according to dynamic learning situations. In this paper, we propose a context-aware mobile learning system called Collaborative and Context-aware Mobile Learning System (CCMLS). It takes into account the requirements of Mobility, Collaboration and Context-Awareness. This system is based on the semantic modeling of the learning context and the learning content. The adaptation part of this approach is made up of adaptation rules to propose and select relevant resources, learning partners and learning activities based not only on the user’s needs, but also on its current context.

Keywords: mobile learning, mobile technologies, context-awareness, collaboration, semantic web, adaptation engine, adaptation strategy, learning object, learning context

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Authors: Kamal Amarouche, Houda Benbrahim, Ismail Kassou

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Nowadays, e-commerce shopping websites have experienced noticeable growth. These websites have gained consumers’ trust. After purchasing a product, many consumers share comments where opinions are usually embedded about the given product. Research on the automatic management of opinions that gives suggestions to potential consumers and portrays an image of the product to manufactures has been growing recently. After launching the product in the market, the reviews generated around it do not usually contain helpful information or generic opinions about this product (e.g. telephone: great phone...); in the sense that the product is still in the launching phase in the market. Within time, the product becomes old. Therefore, consumers perceive the advantages/ disadvantages about each specific product feature. Therefore, they will generate comments that contain their sentiments about these features. In this paper, we present an unsupervised method to extract different product features hidden in the opinions which influence its purchase, and that combines Time Weighting (TW) which depends on the time opinions were expressed with Term Frequency-Inverse Document Frequency (TF-IDF). We conduct several experiments using two different datasets about cell phones and hotels. The results show the effectiveness of our automatic feature extraction, as well as its domain independent characteristic.

Keywords: opinion mining, product feature extraction, sentiment analysis, SentiWordNet

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Authors: A. Hol

Abstract:

21^st century brought waves of business and industry eTransformations. The impact of change is also being seen in education. To identify the extent of this, scenario analysis methodology was utilised with the aim to assess business transformations across industry sectors ranging from craftsmanship, medicine, finance and manufacture to innovations and adoptions of new technologies and business models. Firstly, scenarios were drafted based on the current eTransformation models and its dimensions. Following this, eTransformation framework was utilised with the aim to derive the key eTransformation parameters, the essential characteristics that have enabled eTransformations across the sectors. Following this, identified key parameters were mapped to the transforming domain-education. The mapping assisted in deriving a cognitive eTransformation framework for education sector. The framework highlights the importance of context and the notion that education today needs not only to deliver content to students but it also needs to be able to meet the dynamically changing demands of specific student and industry groups. Furthermore, it pinpoints that for such processes to be supported, specific technology is required, so that instant, on demand and periodic feedback as well as flexible, dynamically expanding study content can be sought and received via multiple education mediums.

Keywords: education sector, business transformation, eTransformation model, cognitive model, cognitive systems, eTransformation

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Authors: Anna Bohers, Emília Hroncová, Juraj Ladomerský

Abstract:

Oil sludge with its main characteristic of high acidity is a waste product generated from the operation of refinery and petrochemical plants. Former refinery and petrochemical plant - Petrochema Dubová is present in Slovakia as well. Its activities was to process the crude oil through sulfonation and adsorption technology for production of lubricating and special oils, synthetic detergents and special white oils for cosmetic and medical purposes. Seventy years ago – period, when this historical acid sludge burden has been created – comparing to the environmental awareness the production was in preference. That is the reason why, as in many countries, also in Slovakia a historical environmental burden is present until now – 229 211 m3 of oil sludge in the middle of the National Park of Nízke Tatry mountain chain. Neither one of tried treatment methods – bio or non-biologic one - was proved as suitable for processing or for recovery in the reason of different factors admission: i.e. strong aggressivity, difficulty with handling because of its sludgy and liquid state et sim. As a potential solution, also incineration was tested, but it was not proven as a suitable method, as the concentration of SO2 in combustion gases was too high, and it was not possible to decrease it under the acceptable value of 2000 mg.mn-3. That is the reason why the operation of incineration plant has been terminated, and the acid sludge landfills are present until nowadays. The objective of this paper is to present a new possibility of processing and valorization of acid sludgy-waste. The processing of oil sludge was performed through the effective separation - thermal desorption technology, through which it is possible to split the sludgy material into the matrix (soil, sediments) and organic contaminants. In order to boost the efficiency in the processing of acid sludge through thermal desorption, the work will present the possibility of application of an original technology – Method of Blowing Decomposition for recovering of organic matter into technological lubricating oil.

Keywords: hazardous waste, oil sludge, remediation, thermal desorption

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Authors: Tatiana R. Simonyan, Elena A. Protasova, Anastasia V. Mamontova, Eugene G. Maksimov, Konstantin A. Lukyanov, Alexey M. Bogdanov

Abstract:

Here, we describe two variants of the calcium indicators based on the GCaMP sensitive core and BrUSLEE fluorescent protein (GCaMP-BrUSLEE and GCaMP-BrUSLEE-145). In contrast to the conventional GCaMP6-family indicators, these fluorophores are characterized by the well-marked responsiveness of their fluorescence decay kinetics to external calcium concentration both in vitro and in cellulo. Specifically, we show that the purified GCaMP-BrUSLEE and GCaMP-BrUSLEE-145 exhibit three-component fluorescence decay kinetics, with the amplitude-normalized lifetime component (t3*A3) of GCaMP-BrUSLEE-145 changing four-fold (500-2000 a.u.) in response to a Ca²⁺ concentration shift in the range of 0—350 nM. Time-resolved fluorescence microscopy of live cells displays the two-fold change of the GCaMP-BrUSLEE-145 mean lifetime upon histamine-stimulated calcium release. The aforementioned Ca²⁺-dependence calls considering the GCaMP-BrUSLEE-145 as a prospective Ca²⁺-indicator with the signal read-out in the time domain.

Keywords: calcium imaging, fluorescence lifetime imaging microscopy, fluorescent proteins, genetically encoded indicators

Procedia PDF Downloads 158

Authors: Roba Zakariah Shaheen

Abstract:

In the 21st-century Interior, design pedagogy has developed rapidly due to social and economical factors. Socially, this paper presents research findings that shows a significant relationship between educators and students in interior design education. It shows that students’ personal traits, design process, and thinking process are significantly interrelated. Constructively, this paper presented how personal traits can guide educators in the interior design education domain to develop students’ thinking process. In the same time, it demonstrated how students should use their own personal traits to create their own design process. Constructivism was the theory underneath this research, as it supports the grounded theory, which is the methodological approach of this research. Moreover, Mayer’s Briggs Type Indicator strategy was used to investigate the personality traits scientifically, as a psychological strategy that related to cognitive ability. Conclusions from this research strongly recommends that educators and students should utilize their personal traits to foster interior design education.

Keywords: interior design, pedagogy, constructivism, grounded theory, personality traits, creativity

Procedia PDF Downloads 207

Authors: Elnaz Lashgari, Emel Demircan

Abstract:

Electromyography (EMG) is one of the most important interfaces between humans and robots for rehabilitation. Decoding this signal helps to recognize muscle activation and converts it into smooth motion for the robots. Detecting each muscle’s pattern during walking and running is vital for improving the quality of a patient’s life. In this study, EMG data from 10 muscles in 10 subjects at 4 different speeds were analyzed. EMG signals are nonlinear with high dimensionality. To deal with this challenge, we extracted some features in time-frequency domain and used manifold learning and Laplacian Eigenmaps algorithm to find the intrinsic features that represent data in low-dimensional space. We then used the Bayesian classifier to identify various patterns of EMG signals for different muscles across a range of running speeds. The best result for vastus medialis muscle corresponds to 97.87±0.69 for sensitivity and 88.37±0.79 for specificity with 97.07±0.29 accuracy using Bayesian classifier. The results of this study provide important insight into human movement and its application for robotics research.

Keywords: electromyography, manifold learning, ISOMAP, Laplacian Eigenmaps, locally linear embedding

Procedia PDF Downloads 363