Search results for: deep acting

Authors: Tahar Alimi, Rahma Boujebane, Wiem Derouich, Lamia Hadrich Belguith

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Machine translation task of low-resourced languages such as Arabic is a challenging task. Despite the appearance of sophisticated models based on the latest deep learning techniques, namely the transfer learning and transformers, all models prove incapable of carrying out an acceptable translation, which includes Arabic Dialects (AD), because they do not have official status. In this paper, we present a machine translation model designed to translate Arabic multidialectal content into Modern Standard Arabic (MSA), leveraging both new and existing parallel resources. The latter achieved the best results for both Levantine and Maghrebi dialects with a BLEU score of 64.99.

Keywords: Arabic translation, dialect translation, fine-tune, MSA translation, transformer, translation

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Authors: Sanjeev Sharma

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We are living in the information age. Information is not only important to an individual but also to researchers, scientists, academicians and all others who are doing work in their respective fields. The 21st century which is also known as the electronic era has brought several changes in the mechanism of the libraries in their working environment. In the present scenario, acquisition of information resources and implementation of new strategies have brought a revolution in the library’s structures and their principles. In the digital era, the role of the library has become important as new information is coming at every minute. The knowledge society wants to seek information at their desk. The libraries are managing electronic services and web-based information sources constantly in a democratic way. The basic objective of every library is to save the time of user which is based on the quality and user-orientation of services. With the advancement of information communication and technology, the libraries should pay more devotion to the development trends of the information society that would help to adjust their development strategies and information needs of the knowledge society. The knowledge-based society demands to re-define the position and objectives of all the institutions which work with information, knowledge, and culture. The situation is the era of digital India is changing at a fast speed. Everyone wants information 24x7 and libraries have been recognized as one of the key elements for open access to information, which is crucial not only to individual but also to democratic knowledge-based information society. Libraries are especially important now a day the whole concept of education is focusing more and more independent e-learning and their acting. The citizens of India must be able to find and use the relevant information. Here we can see libraries enter the stage: The essential features of libraries are to acquire, organize, store and retrieve for use and preserve publicly available material irrespective of the print as well as non-print form in which it is packaged in such a way that, when it is needed, it can be found and put to use.

Keywords: knowledge, society, libraries, culture

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Authors: Julio Vega

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Expression recognition is a field of Artificial Intelligence whose main objectives are to recognize basic forms of affective expression that appear on people’s faces and contributing to behavioral studies. In this work, a ROS node has been developed that, based on Deep Learning techniques, is capable of detecting the facial expressions of the people that appear in the image. These algorithms were optimized so that they can be executed in real time on an embedded platform. The experiments were carried out in a PC with a USB camera and in a Raspberry Pi 4 with a PiCamera. The final results shows a plausible system, which is capable to work in real time even in an embedded platform.

Keywords: python, low-cost, raspberry pi, emotion detection, human-robot interaction, ROS node

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Authors: E. Kempen, M. J. Labuschagne, M. P. Jama

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There is evidence that experiential learning is truly influential and favored by the millennial generation. However, little is known about the role and attributes an educator has to adopt during the experiential learning cycle, especially when applied in optometry education. This study aimed to identify the roles and attributes of an optometry educator during the different modes of the experiential learning cycle. Methods: A qualitative case study design was used. Data was collected using an open-ended questionnaire survey, following the application of nine different teaching-learning methods based on the experimental learning cycle. The total sample population of 68 undergraduate students from the Department of Optometry at the University of the Free State, South Africa were invited to participate. Focus group interviews (n=15) added additional data that contributed to the interpretation and confirmation of the data obtained from the questionnaire surveys. Results: The perceptions and experiences of the students identified a variety of roles and attributes as well as recommendations on the effective adoption of these roles and attributes. These roles and attributes included being knowledgeable, creating an interest, providing guidance, being approachable, building confidence, implementing ground rules, leading by example, and acting as a mediator. Conclusion: The findings suggest that the actions of an educator have the most substantial impact on students’ perception of a learning experience. Not only are the recommendations based on the views of a millennial generation, but the implementation of the personalized recommendations may also transform a learning environment. This may lead an optometry student to a deeper understanding of knowledge.

Keywords: experiences and perceptions, experiential learning, millennial generation, recommendation for optometry education

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Authors: Charles Lee

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The Principal Orthogonal Decomposition (POD) technique has been used as a model reduction tool for many applications in engineering and science. In principle, one begins with an ensemble of data, called snapshots, collected from an experiment or laboratory results. The beauty of the POD technique is that when applied, the entire data set can be represented by the smallest number of orthogonal basis elements. It is the such capability that allows us to reduce the complexity and dimensions of many physical applications. Mathematical formulations and numerical schemes for the POD method will be discussed along with applications in NASA’s Deep Space Large Antenna Arrays, Satellite Image Reconstruction, Cancer Detection with DNA Microarray Data, Maximizing Stock Return, and Medical Imaging.

Keywords: reduced-order methods, principal component analysis, cancer detection, image reconstruction, stock portfolios

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Authors: Xiangming Ge, Bing Pan, Wei He, Hao Chen, Yong Zhou, Jiayao Wu, Weijiang Chu

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How the extraction and penetration of spudcan affect the performance of the adjacent pile foundation supporting the jacket platform was studied in the program FLAC3D depending on a wind farm project in Bohai sea. The simulations were conducted at the end of the spudcan penetration, which induced a pockmark in the seabed. The effects of the distance between the pile foundation and the pockmark were studied. The displacement at the mudline arose when the pockmark was closer. The bearing capacity of this jacket platform with deep pile foundations has been less influenced by the process of spudcan penetration, which can induce severe stresses on the pile foundation. The induced rotation was also satisfied with the rotation-controlling criteria.

Keywords: offshore foundation, pile-soil interaction, spudcan penetration, FLAC3D

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Authors: Abdullah Alotaiq

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The transition to low-carbon power generation is essential for mitigating climate change and achieving sustainability. This process, however, entails considerable costs, and understanding the factors influencing these costs is critical. This is necessary to cater to the increasing demand for low-carbon electricity across the heating, industry, and transportation sectors. A crucial aspect of this transition is identifying cost-effective and feasible paths for decarbonization, which is integral to global climate mitigation efforts. It is concluded that hybrid solutions, combining different low-carbon technologies, are optimal for minimizing costs and enhancing flexibility. These solutions also address the challenges associated with phasing out existing fossil fuel-based power plants and broadening the spectrum of low-carbon power generation options.

Keywords: review, power generation, energy transition, decarbonisation

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Authors: Laura Rodriguez Amaya

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Advances in science, technology, engineering, and mathematics (STEM) are viewed as important to countries’ national economies and their capacities to be competitive in the global economy. However, many countries experience low numbers of students entering these disciplines. To strengthen the professional STEM pipelines, it is important that students are retained in these disciplines at universities. Scholars agree that to retain students in universities’ STEM degrees, it is necessary that STEM course content shows the relevance of these academic fields to their daily lives. By increasing students’ understanding on the importance of these degrees and careers, students’ motivation to remain in these academic programs can also increase. An effective way to make STEM content relevant to students’ lives is the use of geospatial technologies and geovisualization in the classroom. The Geospatial Revolution, and the science and technology associated with it, has provided scientists and engineers with an incredible amount of data about Earth and Earth systems. This data can be used in the classroom to support instruction and make content relevant to all students. The purpose of this study was to find out the prevalence use of geospatial technologies and geovisualization as teaching practices in a USA university. The Teaching Practices Inventory survey, which is a modified version of the Carl Wieman Science Education Initiative Teaching Practices Inventory, was selected for the study. Faculty in the STEM disciplines that participated in a summer learning institute at a 4-year university in the USA constituted the population selected for the study. One of the summer learning institute’s main purpose was to have an impact on the teaching of STEM courses, particularly the teaching of gateway courses taken by many STEM majors. The sample population for the study is 97.5 of the total number of summer learning institute participants. Basic descriptive statistics through the Statistical Package for the Social Sciences (SPSS) were performed to find out: 1) The percentage of faculty using geospatial technologies and geovisualization; 2) Did the faculty associated department impact their use of geospatial tools?; and 3) Did the number of years in a teaching capacity impact their use of geospatial tools? Findings indicate that only 10 percent of respondents had used geospatial technologies, and 18 percent had used geospatial visualization. In addition, the use of geovisualization among faculty of different disciplines was broader than the use of geospatial technologies. The use of geospatial technologies concentrated in the engineering departments. Data seems to indicate the lack of incorporation of geospatial tools in STEM education. The use of geospatial tools is an effective way to engage students in deep STEM learning. Future research should look at the effect on student learning and retention in science and engineering programs when geospatial tools are used.

Keywords: engineering education, geospatial technology, geovisualization, STEM

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Authors: Tayo P. Ogundunmade, Adedayo A. Adepoju

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Terrorist attacks in liberal democracies bring about a few pessimistic results, for example, sabotaged public support in the governments they target, disturbing the peace of a protected environment underwritten by the state, and a limitation of individuals from adding to the advancement of the country, among others. Hence, seeking for techniques to understand the different factors involved in terrorism and how to deal with those factors in order to completely stop or reduce terrorist activities is the topmost priority of the government in every country. This research aim is to develop an efficient deep learning-based predictive model for the prediction of future terrorist activities in Nigeria, addressing low-quality prediction accuracy problems associated with the existing solution methods. The proposed predictive AI-based model as a counterterrorism tool will be useful by governments and law enforcement agencies to protect the lives of individuals in society and to improve the quality of life in general. A Heterogeneous Bayesian Neural Network (HETBNN) model was derived with Gaussian error normal distribution. Three primary transfer functions (HOTTFs), as well as two derived transfer functions (HETTFs) arising from the convolution of the HOTTFs, are namely; Symmetric Saturated Linear transfer function (SATLINS ), Hyperbolic Tangent transfer function (TANH), Hyperbolic Tangent sigmoid transfer function (TANSIG), Symmetric Saturated Linear and Hyperbolic Tangent transfer function (SATLINS-TANH) and Symmetric Saturated Linear and Hyperbolic Tangent Sigmoid transfer function (SATLINS-TANSIG). Data on the Terrorist activities in Nigeria gathered through questionnaires for the purpose of this study were used. Mean Square Error (MSE), Mean Absolute Error (MAE) and Test Error are the forecast prediction criteria. The results showed that the HETFs performed better in terms of prediction and factors associated with terrorist activities in Nigeria were determined. The proposed predictive deep learning-based model will be useful to governments and law enforcement agencies as an effective counterterrorism mechanism to understand the parameters of terrorism and to design strategies to deal with terrorism before an incident actually happens and potentially causes the loss of precious lives. The proposed predictive AI-based model will reduce the chances of terrorist activities and is particularly helpful for security agencies to predict future terrorist activities.

Keywords: activation functions, Bayesian neural network, mean square error, test error, terrorism

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Authors: Abu Bakar Siddik

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The July Movement in Bangladesh marked a pivotal moment in the nation’s struggle for democratic freedom and the right to free speech. This movement, driven by citizens, intellectuals, and activists, opposed authoritarian governance and the violation of civil liberties. By encouraging support for democratic reforms, it significantly changed the political landscape and highlighted the importance of grassroots activism for human rights. This essay examines the sociopolitical dynamics of the July Movement and its roots in popular resistance to authoritarian rule. It explores the movement's beginnings, emphasizing how citizens, scholars, and activists united to challenge the regime that restricted freedom of speech. In order to show how the movement gathered support for democratic reforms and ultimately helped bring about the overthrow of the regime, the article examines significant demonstrations, speeches, and government acts. This book offers a thorough examination of how the July Movement changed Bangladesh's political landscape by acting as a revolution for free speech and a trigger for the overthrow of autocratic authority, using historical documents, media coverage, and firsthand recollections. This study provides insightful information about how grassroots activism advances human rights.

Keywords: July movement, Bangladesh, free speech, democracy, authoritarianism, civil liberties, political change, human rights, social movements, protests, political landscape, regime change, activism, socio-political dynamics

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Authors: Deybith Venegas-Rojas, Sercan Keskin, Svenja Riekeberg, Sana Azim, Stephanie Manz, R. J. Dwayne Miller, Hoc Khiem Trieu

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Liquid Transmission Electron Microscopy (TEM) is a growing area with a broad range of applications from physics and chemistry to material engineering and biology, in which it is possible to image in-situ unseen phenomena. For this, a nanofluidic device is used to insert the nanoflow with the sample inside the microscope in order to keep the liquid encapsulated because of the high vacuum. In the last years, Si3N4 windows have been widely used because of its mechanical stability and low imaging contrast. Nevertheless, the pressure difference between the inside fluid and the outside vacuum in the TEM generates bulging in the windows. This increases the imaged fluid volume, which decreases the signal to noise ratio (SNR), limiting the achievable spatial resolution. With the proposed device, the membrane is fortified with a microstructure capable of stand higher pressure differences, and almost removing completely the bulging. A theoretical study is presented with Finite Element Method (FEM) simulations which provide a deep understanding of the membrane mechanical conditions and proves the effectiveness of this novel concept. Bulging and von Mises Stress were studied for different membrane dimensions, geometries, materials, and thicknesses. The microfabrication of the device was made with a thin wafer coated with thin layers of SiO2 and Si3N4. After the lithography process, these layers were etched (reactive ion etching and buffered oxide etch (BOE) respectively). After that, the microstructure was etched (deep reactive ion etching). Then the back side SiO2 was etched (BOE) and the array of free-standing micro-windows was obtained. Additionally, a Pyrex wafer was patterned with windows, and inlets/outlets, and bonded (anodic bonding) to the Si side to facilitate the thin wafer handling. Later, a thin spacer is sputtered and patterned with microchannels and trenches to guide the nanoflow with the samples. This approach reduces considerably the common bulging problem of the window, improving the SNR, contrast and spatial resolution, increasing substantially the mechanical stability of the windows, allowing a larger viewing area. These developments lead to a wider range of applications of liquid TEM, expanding the spectrum of possible experiments in the field.

Keywords: liquid cell, liquid transmission electron microscopy, nanofluidics, nanofluidic cell, thin films

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Authors: Fajana Sola Isaac

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In the last two decades, we have witnessed an increased interest in exchange rate pass-through (ERPT) in developing economies and emerging markets. This is perhaps due to the acknowledged significance of the pattern of exchange rate pass-through as a key instrument in monetary policy design, principally in retort to a shock in exchange rate in literature. This paper analyzed Exchange Rate Pass-Through by A Model of Oil Prices and Asymmetric Exchange Rate Fluctuations in Selected African Countries. The study adopted A Non-Linear Autoregressive Distributed Lag approach using yearly data on Algeria, Burundi, Nigeria and South Africa from 1986 to 2022. The paper found asymmetry in exchange rate pass-through in net oil-importing and net oil-exporting countries in the short run during the period under review. An ERPT exhibited a complete pass-through in the short run in the case of net oil-importing countries but an incomplete pass-through in the case of the net oil-exporting countries that were examined. An extended result revealed a significant impact of oil price shock on exchange rate pass-through to domestic price in the long run only for net oil importing countries. The Wald restriction test also confirms the evidence of asymmetric with the role of oil price acting as an accelerator to exchange rate pass-through to domestic price in the countries examined. The study found the outcome to be very useful for gaining expansive knowledge on the external shock impact on ERPT and could be of critical value for national monetary policy decisions on inflation targeting, especially for countries examined and other developing net oil importers and exporters.

Keywords: pass through, exchange rate, ARDL, monetary policy

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Authors: Mustapha Kamel Mihoubi, Mohamed Essadik Kerkar

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Probabilistic risk analysis models are used to provide a better understanding of the reliability and structural failure of works, including when calculating the stability of large structures to a major risk in the event of an accident or breakdown. This work is interested in the study of the probability of failure of concrete dams through the application of reliability analysis methods including the methods used in engineering. It is in our case, the use of level 2 methods via the study limit state. Hence, the probability of product failures is estimated by analytical methods of the type first order risk method (FORM) and the second order risk method (SORM). By way of comparison, a level three method was used which generates a full analysis of the problem and involves an integration of the probability density function of random variables extended to the field of security using the Monte Carlo simulation method. Taking into account the change in stress following load combinations: normal, exceptional and extreme acting on the dam, calculation of the results obtained have provided acceptable failure probability values which largely corroborate the theory, in fact, the probability of failure tends to increase with increasing load intensities, thus causing a significant decrease in strength, shear forces then induce a shift that threatens the reliability of the structure by intolerable values of the probability of product failures. Especially, in case the increase of uplift in a hypothetical default of the drainage system.

Keywords: dam, failure, limit-state, monte-carlo, reliability, probability, simulation, sliding, taylor

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Authors: Abdessalam Hijab, Hafida Boulekbache, Eric Henry

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According to several research studies, the city as a whole is a complex, spatially organized system; its modeling must take into account several factors, socio-economic, and political, or geographical, acting at multiple scales of observation according to varied temporalities. Sustainable management and protection of the environment in this complex system require significant human and technical investment, particularly for monitoring and maintenance. The objective of this paper is to propose an intelligent approach based on the coupling of Geographic Information System (GIS) and Information and Communications Technology (ICT) tools in order to integrate the inhabitants in the processes of sustainable management and protection of the urban environment, specifically in the processes of household waste collection in urban areas. We are discussing a collaborative 'city/inhabitant' space. Indeed, it is a geo-collaborative approach, based on the spatialization and real-time geo-localization of topological and multimedia data taken by the 'active' inhabitant, in the form of geo-localized alerts related to household waste issues in their city. Our proposal provides a good understanding of the extent to which civil society (inhabitants) can help and contribute to the development of complementary solutions for the collection of household waste and the protection of the urban environment. Moreover, it allows the inhabitant to contribute to the enrichment of a data bank for future uses. Our geo-collaborative model will be tested in the Lamkansa sampling district of the city of Casablanca in Morocco.

Keywords: geographic information system, GIS, information and communications technology, ICT, geo-collaboration, inhabitants, city

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Authors: Ihsan Burak Cam, Ayhan Topuz

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Turkish delight has been produced by bleaching the plain delight mix (refined sugar, water and starch) via soapwort extract and powdered sugar. Soapwort extract which contains high amount of saponin, is an additive used in Turkish delight and tahini halvah production to improve consistency, chewiness and color due to its bioactive saponin content by acting as emulsifier. In this study, soapwort powder has been produced by determining optimum process conditions of soapwort extract by using response-surface method. This extract has been enriched with saponin by reverse osmosis (contains %63 saponin in dry bases). Büchi mini spray dryer B-290 was used to produce spray-dried soapwort powder (aw=0.254) from the enriched soapwort concentrate. Processing steps optimization and saponin content enrichment of soapwort extract has been tested on Turkish Delight production. Delight samples, produced by soapwort powder and commercial extract (control), were compared in chewiness, springiness, stickiness, adhesiveness, hardness, color and sensorial characteristics. According to the results, all textural properties except hardness of delights produced by powder were found to be statistically different than control samples. Chewiness, springiness, stickiness, adhesiveness and hardness values of samples (delights produced by the powder / control delights) were determined to be 361.9/1406.7, 0.095/0.251, -120.3/-51.7, 781.9/1869.3, 3427.3g/3118.4g, respectively. According to the quality analysis that has been ran with the end products it has been determined that; there is no statistically negative effect of the soapwort extract and the soapwort powder on the color and the appearance of Turkish Delight.

Keywords: saponin, delight, soapwort powder, spray drying

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Authors: Piroska Biró, Mária Csernoch, János Máth, Kálmán Abari

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Testing the first year students of Informatics at the University of Debrecen revealed that students start their tertiary studies in programming with a low level of programming knowledge and algorithmic skills. The possible reasons which lead the students to this very unfortunate result were examined. The results of the test were compared to the students’ results in the school leaving exams and to their self-assessment values. It was found that there is only a slight connection between the students’ results in the test and in the school leaving exams, especially at intermediate level. Beyond this, the school leaving exams do not seem to enable students to evaluate their own abilities.

Keywords: deep and surface approaches, metacognitive abilities, programming and algorithmic skills, school leaving exams, tracking code

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Authors: Shirley Kuo

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The main purpose of this study is to develop sustainable indicators for Taiwan, and using the Delphi method to find that our tourist areas can progress in a sustainable way. We need a lot of infrastructures and policies to develop tourist areas, and with proper KPI indicators can reduce the destruction of the natural and ecological environment. This study will first study the foreign certification experiences, because Taiwan is currently in the development stage, and then the methodology will explain in-depth interviews using the Delphi method, and then there is discussion about which KPI indicators Taiwan currently needs. In this study current progress is a deep understanding of national sustainable tourism certification and KPI indicators.

Keywords: sustainable tourism, certification, KPI indicators, Delphi method

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Authors: Despoina Mouratidis, Katia Lida Kermanidis

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Machine translation (MT) is a very important task in Natural Language Processing (NLP). MT evaluation is crucial in MT development, as it constitutes the means to assess the success of an MT system, and also helps improve its performance. Several methods have been proposed for the evaluation of (MT) systems. Some of the most popular ones in automatic MT evaluation are score-based, such as the BLEU score, and others are based on lexical similarity or syntactic similarity between the MT outputs and the reference involving higher-level information like part of speech tagging (POS). This paper presents a language-independent machine learning framework for classifying pairwise translations. This framework uses vector representations of two machine-produced translations, one from a statistical machine translation model (SMT) and one from a neural machine translation model (NMT). The vector representations consist of automatically extracted word embeddings and string-like language-independent features. These vector representations used as an input to a multi-layer neural network (NN) that models the similarity between each MT output and the reference, as well as between the two MT outputs. To evaluate the proposed approach, a professional translation and a "ground-truth" annotation are used. The parallel corpora used are English-Greek (EN-GR) and English-Italian (EN-IT), in the educational domain and of informal genres (video lecture subtitles, course forum text, etc.) that are difficult to be reliably translated. They have tested three basic deep learning (DL) architectures to this schema: (i) fully-connected dense, (ii) Convolutional Neural Network (CNN), and (iii) Long Short-Term Memory (LSTM). Experiments show that all tested architectures achieved better results when compared against those of some of the well-known basic approaches, such as Random Forest (RF) and Support Vector Machine (SVM). Better accuracy results are obtained when LSTM layers are used in our schema. In terms of a balance between the results, better accuracy results are obtained when dense layers are used. The reason for this is that the model correctly classifies more sentences of the minority class (SMT). For a more integrated analysis of the accuracy results, a qualitative linguistic analysis is carried out. In this context, problems have been identified about some figures of speech, as the metaphors, or about certain linguistic phenomena, such as per etymology: paronyms. It is quite interesting to find out why all the classifiers led to worse accuracy results in Italian as compared to Greek, taking into account that the linguistic features employed are language independent.

Keywords: machine learning, machine translation evaluation, neural network architecture, pairwise classification

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Authors: Vinai K. Singh

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In Neural Network-based Learning techniques, there are several models of Convolutional Networks. Whenever the methods are deployed with large datasets, only then can their applicability and appropriateness be determined. Clinical and pathological pictures of lobular carcinoma are thought to exhibit a large number of random formations and textures. Working with such pictures is a difficult problem in machine learning. Focusing on wet laboratories and following the outcomes, numerous studies have been published with fresh commentaries in the investigation. In this research, we provide a framework that can operate effectively on raw photos of various resolutions while easing the issues caused by the existence of patterns and texturing. The suggested approach produces very good findings that may be used to make decisions in the diagnosis of cancer.

Keywords: lobular carcinoma, convolutional neural networks (CNN), deep learning, histopathological imagery scans

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Authors: Cheng-Jui Li, Chien-Chou Tseng

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This study adopts Computational Fluid Dynamics (CFD) technique to build the multiphase flow numerical model where the interface between the flue gas and desulfurization liquid can be traced by Eulerian-Eulerian model. Inside the tower, the contact of the desulfurization liquid flow from the spray nozzles and flue gas flow can trigger chemical reactions to remove the sulfur dioxide from the exhaust gas. From experimental observations of the industrial scale plant, the desulfurization mechanism depends on the mixing level between the flue gas and the desulfurization liquid. In order to significantly improve the desulfurization efficiency, the mixing efficiency and the residence time can be increased by perforated sieve trays. Hence, the purpose of this research is to investigate the flow structure of sieve trays for the flue gas desulfurization by numerical simulation. In this study, there is an outlet at the top of FGD tower to discharge the clean gas and the FGD tower has a deep tank at the bottom, which is used to collect the slurry liquid. In the major desulfurization zone, the desulfurization liquid and flue gas have a complex mixing flow. Because there are four perforated plates in the major desulfurization zone, which spaced 0.4m from each other, and the spray array is placed above the top sieve tray, which includes 33 nozzles. Each nozzle injects desulfurization liquid that consists of the Mg(OH)2 solution. On each sieve tray, the outside diameter, the hole diameter, and the porosity are 0.6m, 20 mm and 34.3%. The flue gas flows into the FGD tower from the space between the major desulfurization zone and the deep tank can finally become clean. The desulfurization liquid and the liquid slurry goes to the bottom tank and is discharged as waste. When the desulfurization solution flow impacts the sieve tray, the downward momentum will be converted to the upper surface of the sieve tray. As a result, a thin liquid layer can be developed above the sieve tray, which is the so-called the slurry layer. And the volume fraction value within the slurry layer is around 0.3~0.7. Therefore, the liquid phase can't be considered as a discrete phase under the Eulerian-Lagrangian framework. Besides, there is a liquid column through the sieve trays. The downward liquid column becomes narrow as it interacts with the upward gas flow. After the flue gas flows into the major desulfurization zone, the flow direction of the flue gas is upward (+y) in the tube between the liquid column and the solid boundary of the FGD tower. As a result, the flue gas near the liquid column may be rolled down to slurry layer, which developed a vortex or a circulation zone between any two sieve trays. The vortex structure between two sieve trays results in a sufficient large two-phase contact area. It also increases the number of times that the flue gas interacts with the desulfurization liquid. On the other hand, the sieve trays improve the two-phase mixing, which may improve the SO2 removal efficiency.

Keywords: Computational Fluid Dynamics (CFD), Eulerian-Eulerian Model, Flue Gas Desulfurization (FGD), perforated sieve tray

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Authors: Jalal Khan, Daniyal Ali Sehrai, Shakeel Ahmad

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This paper presents the design and performance evaluation of multiband metamaterial based antenna operating in the 3.6 GHz (4G), 14.33 GHz, and 28.86 GHz (5G) frequency bands, for future mobile and handheld devices. The radiating element of the proposed design is made up of a conductive material supported by a 1.524 mm thicker Rogers-4003 substrate, having a relative dielectric constant and loss tangent of 3.55 and 0.0027, respectively. The substrate is backed by truncated ground plane. The future mobile communication system is based on higher frequencies, which are highly affected by the atmospheric conditions. Therefore, to overcome the path loss problem, essential enhancements and improvements must be made in the overall performance of the antenna. The traditional ground plane does not provide the in-phase reflection and surface wave suppression due to which side and back lobes are produced. This will affect the antenna performance in terms of gain and efficiency. To enhance the overall performance of the antenna, a metamaterial acting as a high impedance surface (HIS) is used as a reflector in the proposed design. The simulated gain of the metamaterial based antenna is enhanced from {2.76-6.47, 4.83-6.71 and 7.52-7.73} dB at 3.6, 14.33 and 28.89 GHz, respectively relative to the gain of the antenna backed by a traditional ground plane. The proposed antenna radiated efficiently with a radiated efficiency (>85 %) in all the three frequency bands with and without metamaterial surface. The total volume of the antenna is (L x W x h=45 x 40 x 1.524) mm³. The antenna can be potentially used for wireless handheld devices and mobile terminal. All the simulations have been performed using the Computer Simulation Technology (CST) software.

Keywords: CST MWS, fourth generation/fifth generation, 4G/5G, high gain, multiband, metamaterial

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Authors: Alexis Aldo Mendoza Villarroel, Ademir Clemente Villena Zevallos, Cristian Jose Lopez Del Alamo

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With the advancement of technology it is now possible to scan entire objects and obtain their digital representation by using point clouds or polygon meshes. However, some objects may be broken or have missing parts; thus, several methods focused on this problem have been proposed based on Geometric Deep Learning, such as GCNN, ACNN, PointNet, among others. In this article an approach from a different paradigm is proposed, using metric data structures to index global descriptors in the spectral domain and allow the recovery of a set of similar models in polynomial time; to later use the Iterative Close Point algorithm and recover the parts of the incomplete model using the geometry and topology of the model with less Hausdorff distance.

Keywords: 3D reconstruction method, point cloud completion, shape completion, similarity search

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Authors: Man Bahadur Shahu

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The secularism is a controversial, debatable and misinterpreted issue since its endorsement in the 2007 constitution in Nepal. The unprecedented acts have been seen favoring and disfavoring against the secularism within the public domain—which creates the fallacies and suspicions in the rationalization and modernization process. This paper highlights three important points: first, the secularization suddenly ruptures the silence and institutional decline of religion within the state. Second, state effort on secularism simultaneously fosters the state neutrality and state separation from religious institutions that amplify the recognition of all religious groups through the equal treatment in their festivity, rituals, and practices. Third, no state would completely secular because of their deep-rooted mindset and disposition with their own religious faiths and beliefs that largely enhance intergroup conflict, dispute, riot and turbulence in post-secular period in the name of proselytizing and conversion.

Keywords: conflict, proselytizing, religion, secular

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Authors: Sumit Kumar, Sunil Kumar, Chandan Deep Singh

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This paper presents modeling and simulation of flexible robot in an underwater environment. The underwater environment completely contrasts with ground or space environment. The robot in an underwater situation is subjected to various dynamic forces like buoyancy forces, hydrostatic and hydrodynamic forces. The underwater robot is modeled as Rayleigh beam. The developed model further allows estimating the deflection of tip in two directions. The complete dynamics of the underwater robot is analyzed, which is the main focus of this investigation. The control of robot trajectory is not discussed in this paper. Simulation is performed using Symbol Shakti software.

Keywords: bond graph modeling, dynamics. modeling, rayleigh beam, underwater robot

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Authors: Turin Datta, Kisor K. Sahu

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Structural materials used in today’s oil and gas exploration and drilling of both onshore and offshore oil and gas wells must possess superior tensile properties, excellent resistance to corrosive degradation that includes general, localized (pitting and crevice) and environment assisted cracking such as stress corrosion cracking and hydrogen embrittlement. The High Pressure and High Temperature (HPHT) wells are typically operated at temperature and pressure that can exceed 300-3500F and 10,000psi (69MPa) respectively which necessitates the use of exotic materials in these exotic sources of natural resources. This research investigation is focussed on the evaluation of tensile properties and corrosion behavior of AISI 4140 High-Strength Low Alloy Steel (HSLA) possessing tempered martensitic microstructure and Duplex 2205 Stainless Steel (DSS) having austenitic and ferritic phase. The selection of this two alloys are primarily based on economic considerations as 4140 HSLA is cheaper when compared to DSS 2205. Due to the harsh aggressive chemical species encountered in deep oil and gas wells like chloride ions (Cl-), carbon dioxide (CO2), hydrogen sulphide (H2S) along with other mineral organic acids, DSS 2205, having a dual-phase microstructure can mitigate the degradation resulting from the presence of both chloride ions (Cl-) and hydrogen simultaneously. Tensile properties evaluation indicates a ductile failure of DSS 2205 whereas 4140 HSLA exhibit quasi-cleavage fracture due to the phenomenon of ‘tempered martensitic embrittlement’. From the potentiodynamic polarization testing, it is observed that DSS 2205 has higher corrosion resistance than 4140 HSLA; the former exhibits passivity signifying resistance to localized corrosion while the latter exhibits active dissolution in all the environmental parameters space that was tested. From the Scanning Electron Microscopy (SEM) evaluation, it is understood that stable pits appear in DSS 2205 only when the temperature exceeds the critical pitting temperature (CPT). SEM observation of the corroded 4140 HSLA specimen tested in aqueous 3.5 wt.% NaCl solution reveals intergranular cracking which appears due to the adsorption and diffusion of hydrogen during polarization, thus, causing hydrogen-induced cracking/hydrogen embrittlement. General corrosion testing of DSS 2205 in acidic brine (pH~3.0) solution at ambient temperature using coupons indicate no weight loss even after three months whereas the corrosion rate of AISI 4140 HSLA is significantly higher after one month of testing.

Keywords: DSS 2205, polarization, pitting, SEM

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Authors: Hannan Al-Qanai, Haitham Mustafa, Rajeswaran Sivasankar

Abstract:

Kuwait Integrated Petroleum Industries Company (KIPIC, Company), established in 2016 as a subsidiary to Kuwait Petroleum Corporation (KPC), is responsible for operating and managing the largest grassroots integrated complex for refining, petrochemicals manufacture businesses, and liquefied natural gas import facilities at Al-Zour, Kuwait. KIPIC and its Contractors/sub-contractors employ over 69,000 staff in its current projects at Al-Zour during peak construction activity. KIPIC holds a unique responsibility to the society, which includes all stakeholders, and demonstrates its social commitment in developing an integrated environment & social management system (ESMS) and ensuring sustainability. This paper mainly demonstrates the knowledge on corporate branding from a corporate social responsibility (CSR) perspective and presents the achievements and best practices of KIPIC in the field of CSR and the challenges faced in handling social issues. Moreover, the study is based on qualitative data abstracted from KIPIC Health, Safety, Security & Environment Management System (HSSE MS) procedures, audit reports, the outcome of counseling sessions, national and international laws and regulations, and International Guidelines on Environment and Social Management System (ESMS). KIPIC has committed to caring for the environmental concerns and acting on social as they do on profits and economic growth. The main findings of this paper are that the successful implementation and operationalization of CSR within an organization depends on a simple but stringent process with both top-down and bottom-up commitment.

Keywords: welfare, corporate social responsibility, social management, sustainability

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Authors: Lin Cheng, Zijiang Yang

Abstract:

Program synthesis is the task to automatically generate programs based on user specification. In this paper, we present a framework that synthesizes programs from flow charts that serve as accurate and intuitive specification. In order doing so, we propose a deep neural network called GRCNN that recognizes graph structure from its image. GRCNN is trained end-to-end, which can predict edge and node information of the flow chart simultaneously. Experiments show that the accuracy rate to synthesize a program is 66.4%, and the accuracy rates to recognize edge and node are 94.1% and 67.9%, respectively. On average, it takes about 60 milliseconds to synthesize a program.

Keywords: program synthesis, flow chart, specification, graph recognition, CNN

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Authors: Sung Uk Park, Young Su Kang, Sangmo Kang, Young Kweon Suh

Abstract:

Mineral product, waste concrete (fine aggregates), waste in the optical field, industry, and construction employ separators to separate solids and classify them according to their size. Various sorting machines are used in the industrial field such as those operating under electrical properties, centrifugal force, wind power, vibration, and magnetic force. Study on separators has been carried out to contribute to the environmental industry. In this study, we perform CFD analysis for understanding the basic mechanism of the separation of waste concrete (fine aggregate) particles from air with a machine built with a rotor with blades. In CFD, we first performed two-dimensional particle tracking for various particle sizes for the model with 1 degree, 1.5 degree, and 2 degree angle between each blade to verify the boundary conditions and the method of rotating domain method to be used in 3D. Then we developed 3D numerical model with ANSYS CFX to calculate the air flow and track the particles. We judged the capability of particle separation for given size by counting the number of particles escaping from the domain toward the exit among 10 particles issued at the inlet. We confirm that particles experience stagnant behavior near the exit of the rotating blades where the centrifugal force acting on the particles is in balance with the air drag force. It was also found that the minimum particle size that can be separated by the machine with the rotor is determined by its capability to stay at the outlet of the rotor channels.

Keywords: environmental industry, separator, CFD, fine aggregate

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Authors: Danilo López, Nelson Vera, Luis Pedraza

Abstract:

This paper analyzes fundamental ideas and concepts related to neural networks, which provide the reader a theoretical explanation of Long Short-Term Memory (LSTM) networks operation classified as Deep Learning Systems, and to explicitly present the mathematical development of Backward Pass equations of the LSTM network model. This mathematical modeling associated with software development will provide the necessary tools to develop an intelligent system capable of predicting the behavior of licensed users in wireless cognitive radio networks.

Keywords: neural networks, multilayer perceptron, long short-term memory, recurrent neuronal network, mathematical analysis

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Authors: Yoshimoto Kurihara, Tad Gonsalves

Abstract:

Currently, in the field of object posture estimation, there is research on estimating the position and angle of an object by storing a 3D model of the object to be estimated in advance in a computer and matching it with the model. However, in this research, we have succeeded in creating a module that is much simpler, smaller in scale, and faster in operation. Our 6D pose estimation model consists of two different networks – a classification network and a regression network. From a single RGB image, the trained model estimates the class of the object in the image, the coordinates of the object, and its rotation angle in 3D space. In addition, we compared the estimation accuracy of each camera position, i.e., the angle from which the object was captured. The highest accuracy was recorded when the camera position was 75°, the accuracy of the classification was about 87.3%, and that of regression was about 98.9%.

Keywords: 6D posture estimation, image recognition, deep learning, AlexNet

Procedia PDF Downloads 159