Search results for: learning methods

Authors: Saba Pajuhan, Afshin Farahbakhsh, S. M. M. Dastgheib

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Biosurfactants and bioemulsifiers are a structurally diverse group of surface-active molecules synthesized by microorganisms, they are amphipathic molecules which reduce surface and interfacial tensions and widely used in pharmaceutical, cosmetic, food and petroleum industries. In this paper, several methods of bioemulsifer synthesis and purification by Bacillus licheniformis strains (namely ACO1, PTCC 1595 and ACO4) were investigated. Strains were grown in nutrient broth with different conditions in order to get maximum production of bioemulsifer. The purification of bio emulsifier and the quality evaluation of the product was done by adding sulfuric acid (H₂SO₄) (98%), Ethanol or HCl to the solution followed by centrifuging. To determine the optimal conditions yielding the highest bioemulsifier production, the effect of various carbon and nitrogen sources, temperature, NaCl concentration, pH, O₂ levels, incubation time are indispensable and all of them were highly effective in bioemulsifiers production.

Keywords: biosurfactant, bioemulsifier, purification, surface tension, interfacial tension

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Authors: Gourg Ebrahim Shafik Eskandar

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The essential role of the digital library in developing museum display methods, recording ancient Egyptian antiquities, facilitating scientific research and storing antiquities in the Grand Egyptian Museum, which helped and saved a lot of time and money spent to equip the Grand Egyptian Museum. The technology of digital library, linking it to ancient Egyptian antiquities and the latest results, which scientific research has reached in the field of libraries and its impact on many areas of tourism and antiquities. The research also aims to show the main role of the digital library and the Arab countries emulating European countries in digitizing libraries and recent developments in Egyptian libraries and their role in many areas of life and linking them to Egyptology. The research will also explain how the museum display methods will be developed in the Grand Egyptian Museum, and the recording of ancient Egyptian antiquities in order to facilitate the process of scientific research and methods of storing antiquities, and it will also work to save time and effort for researchers. The research will also deal with lighting, and its prominent role in the display in the interior design and coordination of the Grand Egyptian Museum, through which the unique artifacts and artifacts displayed can be displayed, and they can be used in a strong or simple form. Depending on the condition of the piece to be displayed. The research will also go to show the role of the digital library in how the Grand Egyptian Museum contains gathering areas and how to distribute spaces, guidance, information, reception, libraries, lecture halls, restaurants, cafeterias, shops, permanent and temporary galleries, and bathrooms.

Keywords: grand egyptian museum, egyptian, museum, egyptian museum

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Authors: Mahtsente Tadese, Lalit Kumar, Richard Koech

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The aim of this study was to project and analyze climate change in the Awash River Basin (ARB) using bias-corrected Global and Regional Climate Model simulations. The analysis included a baseline period from 1986-2005 and two future scenarios (the 2050s and 2070s) under two representative concentration pathways (RCP4.5 and RCP8.5). Bias correction methods were evaluated using graphical and statistical methods. Following the evaluation of bias correction methods, the Distribution Mapping (DM) and Power Transformation (PT) were used for temperature and precipitation projection, respectively. The 2050s and 2070s RCP4 simulations showed an increase in precipitation during half of the months with 32 and 10%, respectively. Moreover, the 2050s and 2070s RCP8.5 simulation indicated a decrease in precipitation with 18 and 26%, respectively. The 2050s and 2070s RCP8.5 simulation indicated a significant decrease in precipitation in four of the months (February/March to May) with the highest decreasing rate of 34.7%. The 2050s and 2070s RCP4.5 simulation showed an increase of 0.48-2.6 °C in maximum temperature. In the case of RCP8.5, the increase rate reached 3.4 °C and 4.1 °C in the 2050s and 2070s, respectively. The changes in precipitation and temperature might worsen the water stress, flood, and drought in ARB. Moreover, the critical focus should be given to mitigation strategies and management options to reduce the negative impact. The findings of this study provide valuable information on future precipitation and temperature change in ARB, which will help in the planning and design of sustainable mitigation approaches in the basin.

Keywords: variability, climate change, Awash River Basin, precipitation

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Authors: Thomas Trouton, Sebastian Tanner, Manvir Sandher

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The use of simulation in undergraduate and postgraduate medical curricula is ever-growing, is a useful addition to the traditional apprenticeship model of learning within medical education, and better prepares graduates for the team-based approach to healthcare seen in real-life clinical practice. As a learning tool, however, undergraduate medical students often have little understanding of the theory behind the use of medical simulation and have little experience in planning and delivering their own simulated teaching sessions. We designed and implemented a student-selected component (SSC) as part of the undergraduate medical curriculum at the University of Buckingham Medical School to introduce students to the concepts behind the use of medical simulation in education and allow them to plan and deliver their own simulated medical scenario to their peers. The SSC took place over a 2-week period in the 3rd year of the undergraduate course. There was a mix of lectures, seminars and interactive group work sessions, as well as hands-on experience in the simulation suite, to introduce key concepts related to medical simulation, including technical considerations in simulation, human factors, debriefing and troubleshooting scenarios. We evaluated the success of our SSC using “Net Promotor Scores” (NPS) to assess students’ confidence in planning and facilitating a simulation-based teaching session, as well as leading a debrief session. In all three domains, we showed an increase in the confidence of the students. We also showed an increase in confidence in the management of common medical emergencies as a result of the SSC. Overall, the students who chose our SSC had the opportunity to learn new skills in medical education, with a particular focus on the use of simulation-based teaching, and feedback highlighted that a number of students would take these skills forward in their own practice. We demonstrated an increase in confidence in several domains related to the use of medical simulation in education and have hopefully inspired a new generation of medical educators.

Keywords: simulation, SSC, teaching, medical students

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Authors: Michael Smalenberger

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Intelligent tutoring systems (ITS) are computer-based platforms which can incorporate artificial intelligence to provide step-by-step guidance as students practice problem-solving skills. ITS can replicate and even exceed some benefits of one-on-one tutoring, foster transactivity in collaborative environments, and lead to substantial learning gains when used to supplement the instruction of a teacher or when used as the sole method of instruction. A common facet of many ITS is their use of Bayesian Knowledge Tracing (BKT) to estimate parameters necessary for the implementation of the artificial intelligence component, and for the probability of mastery of a knowledge component relevant to the ITS. While various techniques exist to estimate these parameters and probability of mastery, none directly and reliably ask the user to self-assess these. In this study, 111 undergraduate students used an ITS in a college-level introductory statistics course for which detailed transaction-level observations were recorded, and users were also routinely asked direct questions that would lead to such a self-assessment. Comparisons were made between these self-assessed values and those obtained using commonly used estimation techniques. Our findings show that such self-assessments are particularly relevant at the early stages of ITS usage while transaction level data are scant. Once a user’s transaction level data become available after sufficient ITS usage, these can replace the self-assessments in order to eliminate the identifiability problem in BKT. We discuss how these findings are relevant to the number of exercises necessary to lead to mastery of a knowledge component, the associated implications on learning curves, and its relevance to instruction time.

Keywords: Bayesian Knowledge Tracing, Intelligent Tutoring System, in vivo study, parameter estimation

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Authors: Joy Chadwick

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There is no doubt that classrooms of today are more complex and diverse than ever before. Preparing teacher candidates to meet these challenges is essential to ensure the retention of teachers within the profession and to ensure that graduates begin their teaching careers with the knowledge and understanding of how to effectively meet the diversity of students they will encounter. Creating inclusive classrooms requires teachers to have a repertoire of effective instructional skills and strategies. Teachers must also have the mindset to embrace diversity and value the uniqueness of individual students in their care. This qualitative study analyzed teacher candidates' experiences as they completed a fourteen-week teaching practicum while simultaneously completing a university course focused on inclusive pedagogy. The research investigated the challenges and successes teacher candidates had in navigating the translation of theory related to inclusive pedagogy into their teaching practice. Applying threshold concept theory as a framework, the research explored the troublesome concepts, liminal spaces, and transformative experiences as connected to inclusive practices. Threshold concept theory suggests that within all disciplinary fields, there exists particular threshold concepts that serve as gateways or portals into previously inaccessible ways of thinking and practicing. It is in these liminal spaces that conceptual shifts in thinking and understanding and deep learning can occur. The threshold concept framework provided a lens to examine teacher candidate struggles and successes with the inclusive education course content and the application of this content to their practicum experiences. A qualitative research approach was used, which included analyzing twenty-nine course reflective journals and six follow up one-to-one semi structured interviews. The journals and interview transcripts were coded and themed using NVivo software. Threshold concept theory was then applied to the data to uncover the liminal or stuck spaces of learning and the ways in which the teacher candidates navigated those challenging places of teaching. The research also sought to uncover potential transformative shifts in teacher candidate understanding as connected to teaching in an inclusive classroom. The findings suggested that teacher candidates experienced difficulties when they did not feel they had the knowledge, skill, or time to meet the needs of the students in the way they envisioned they should. To navigate the frustration of this thwarted vision, they relied on present and previous course content and experiences, collaborative work with other teacher candidates and their mentor teachers, and a proactive approach to planning for students. Transformational shifts were most evident in their ability to reframe their perceptions of children from a deficit or disability lens to a strength-based belief in the potential of students. It was evident that through their course work and practicum experiences, their beliefs regarding struggling students shifted as they saw the value of embracing neurodiversity, the importance of relationships, and planning for and teaching through a strength-based approach. Research findings have implications for teacher education programs and for understanding threshold concepts theory as connected to practice-based learning experiences.

Keywords: inclusion, inclusive education, liminal space, teacher education, threshold concepts, troublesome knowledge

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Authors: Abderrahim Elmoataz

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More and more contemporary applications involve data in the form of functions defined on irregular and topologically complicated domains (images, meshs, points clouds, networks, etc). Such data are not organized as familiar digital signals and images sampled on regular lattices. However, they can be conveniently represented as graphs where each vertex represents measured data and each edge represents a relationship (connectivity or certain affinities or interaction) between two vertices. Processing and analyzing these types of data is a major challenge for both image and machine learning communities. Hence, it is very important to transfer to graphs and networks many of the mathematical tools which were initially developed on usual Euclidean spaces and proven to be efficient for many inverse problems and applications dealing with usual image and signal domains. Historically, the main tools for the study of graphs or networks come from combinatorial and graph theory. In recent years there has been an increasing interest in the investigation of one of the major mathematical tools for signal and image analysis, which are Partial Differential Equations (PDEs) variational methods on graphs. The normalized p-laplacian operator has been recently introduced to model a stochastic game called tug-of-war-game with noise. Part interest of this class of operators arises from the fact that it includes, as particular case, the infinity Laplacian, the mean curvature operator and the traditionnal Laplacian operators which was extensiveley used to models and to solve problems in image processing. The purpose of this paper is to introduce and to study a new class of normalized p-Laplacian on graphs. The introduction is based on the extension of p-harmonious function introduced in as discrete approximation for both infinity Laplacian and p-Laplacian equations. Finally, we propose to use these operators as a framework for solving many inverse problems in image processing.

Keywords: normalized p-laplacian, image processing, stochastic game, inverse problems

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Authors: Changfeng Fu

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Sustainable urban development has been widely accepted a common sense in the modern urban planning and design. However, the measurement and assessment of urban sustainability, especially the quantitative assessment have been always an issue obsessing planning and design professionals. This paper will present an on-going research on the principles and technologies to develop a quantitative urban sustainability assessment principles and techniques which aim to integrate indicators, geospatial and geo-reference data, and assessment techniques together into a mechanism. It is based on the principles and techniques of geospatial analysis with GIS and statistical analysis methods. The decision-making technologies and methods such as AHP and SMART are also adopted to address overall assessment conclusions. The possible interfaces and presentation of data and quantitative assessment results are also described. This research is based on the knowledge, situations and data sources of UK, but it is potentially adaptable to other countries or regions. The implementation potentials of the mechanism are also discussed.

Keywords: urban sustainability assessment, quantitative analysis, sustainability indicator, geospatial data, big data

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Authors: Phillip Kearney, Constantina Lekakou, Stephen Belcher, Alessandro Sordon

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The focus in the automotive industry is to reduce human operator and machine interaction, so manufacturing becomes more automated and safer. The aim is to lower part cost and construction time as well as defects in the parts, sometimes occurring due to the physical limitations of human operators. A move to automate the layup of reinforcement material in composites manufacturing has resulted in the use of tapes that are placed in position by a robotic deposition head, also described as Automated Fibre Placement (AFP). The process of AFP is limited with respect to the finite amount of material that can be loaded into the machine at any one time. Joining two batches of tape material together involves a splice to secure the ends of the finishing tape to the starting edge of the new tape. The splicing method of choice for the majority of prepreg applications is a hand stich method, and as the name suggests requires human input to achieve. This investigation explores three methods for automated splicing, namely, adhesive, binding and stitching. The adhesive technique uses an additional adhesive placed on the tape ends to be joined. Binding uses the binding agent that is already impregnated onto the tape through the application of heat. The stitching method is used as a baseline to compare the new splicing methods to the traditional technique currently in use. As the methods will be used within a High Speed Automated Fibre Placement (HSAFP) process, this meant the parameters of the splices have to meet certain specifications: (a) the splice must be able to endure a load of 50 N in tension applied at a rate of 1 mm/s; (b) the splice must be created in less than 6 seconds, dictated by the capacity of the tape accumulator within the system. The samples for experimentation were manufactured with controlled overlaps, alignment and splicing parameters, these were then tested in tension using a tensile testing machine. Initial analysis explored the use of the impregnated binding agent present on the tape, as in the binding splicing technique. It analysed the effect of temperature and overlap on the strength of the splice. It was found that the optimum splicing temperature was at the higher end of the activation range of the binding agent, 100 °C. The optimum overlap was found to be 25 mm; it was found that there was no improvement in bond strength from 25 mm to 30 mm overlap. The final analysis compared the different splicing methods to the baseline of a stitched bond. It was found that the addition of an adhesive was the best splicing method, achieving a maximum load of over 500 N compared to the 26 N load achieved by a stitching splice and 94 N by the binding method.

Keywords: analysis, automated fibre placement, high speed, splicing

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Authors: Marta Lisowska

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The growing trend of market specialization imposes upon translators the need for proficiency in the working knowledge of specialist discourse. The notion of specialization differs from a broad general category to a highly specialized narrow field. The specialised discourse is used in the channel of communication based upon distinctive features typical for communities of practice whose co-existence is codified and hermetically locked against outsiders. Consequently, any translator deprived of professional discourse competence and social skills is incapable of providing competent translation product from source language into target language. In this paper, we report on research that explores the pedagogical practices aiming to bridge the dichotomy between the professionals and the specialist translators, while accounting for the reality of the world of professional communities entered by undergraduates on two levels: the text-based generic, and the social one. Drawing from the functional social constructivist approach, seen here as situated learning, this paper reports on the case of English-Polish, Polish-English undergraduate BA Level LSP of law translation class run in line with the simulated classroom-based and the reality-based (apprenticeship) approach. This blended method serves the purpose of introducing the young trainees to the professional world. The research provides new insights into how the LSP translation undergraduates become legitimized through discursive and social participation and engagement. The undergraduates, situated peripherally at the outset, experience their own transformation towards becoming members of these professional groups. With subjective evaluation, the trainees take a stance on this dual mode class and development of their skills. Comparing and contrasting their own work done in line with two models of translation teaching: authentic and near-authentic, the undergraduates answer research questions devised by a questionnaire survey The responses take us closer to how students feel about their LSP translation competence development. The major findings show how the trainees perceive the benefits and hardships of their functional translation class. In terms of skills, they related to communication as the most enhanced one; they highly valued the fact of being ‘exposed’ to a variety of texts (cf. multi literalism), team work, learning how to schedule work, IT skills boost and the ability to learn how to work individually. Another finding indicates that students struggled most with specialized language, and co-working with other students. The short-term research shows the momentum when the undergraduate LSP translation trainees entered the path of transformation i.e. gained consciousness of ‘how it is’ to be a participant-translator of real-life communities of practice, gaining pragmatic dint of the social and linguistic skills understood here as discursive competence (text > genre > discourse > professional practice). The undergraduates need to be aware of the work they have to do and challenges they are to face before arriving at the expert level of professional translation competence.

Keywords: communities of practice in LSP translation teaching, learning LSP translation as situated experience, peripheral participation, professional discourse for LSP translation teaching, professional translation competence

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Authors: Goutam Dubey, Varun Dutta

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In the present study, rotary-electro discharge machining of EN31 tool steel has been carried out using a pure copper electrode. Various response variables such as Material Removal Rate (MRR), Tool Wear Rate (TWR), and Machining Rate (MR) have been studied against the selected process variables. The selected process variables were peak current (I), voltage (V), duty cycle, and electrode rotation (N). EN31 Tool Steel is hardened, high carbon steel which increases its hardness and reduces its machinability. Reduced machinability means it not economical to use conventional methods to machine EN31 Tool Steel. So, non-conventional methods play an important role in machining of such materials.

Keywords: electric discharge machining, EDM, tool steel, tool wear rate, optimization techniques

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Authors: Abdullah Al Farwan, Ya Zhang

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In today’s educational arena, it is critical to understand educational data and be able to evaluate important aspects, particularly data on student achievement. Educational Data Mining (EDM) is a research area that focusing on uncovering patterns and information in data from educational institutions. Teachers, if they are able to predict their students' class performance, can use this information to improve their teaching abilities. It has evolved into valuable knowledge that can be used for a wide range of objectives; for example, a strategic plan can be used to generate high-quality education. Based on previous data, this paper recommends employing data mining techniques to forecast students' final grades. In this study, five data mining methods, Decision Tree, JRip, Naive Bayes, Multi-layer Perceptron, and Random Forest with wrapper feature selection, were used on two datasets relating to Portuguese language and mathematics classes lessons. The results showed the effectiveness of using data mining learning methodologies in predicting student academic success. The classification accuracy achieved with selected algorithms lies in the range of 80-94%. Among all the selected classification algorithms, the lowest accuracy is achieved by the Multi-layer Perceptron algorithm, which is close to 70.45%, and the highest accuracy is achieved by the Random Forest algorithm, which is close to 94.10%. This proposed work can assist educational administrators to identify poor performing students at an early stage and perhaps implement motivational interventions to improve their academic success and prevent educational dropout.

Keywords: classification algorithms, decision tree, feature selection, multi-layer perceptron, Naïve Bayes, random forest, students’ academic performance

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Authors: Yan Zhang

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Predictive maintenance is a technique to predict when an in-service machine will fail so that maintenance can be planned in advance. Analytics-driven predictive maintenance is gaining increasing attention in many industries such as manufacturing, utilities, aerospace, etc., along with the emerging demand of Internet of Things (IoT) applications and the maturity of technologies that support Big Data storage and processing. This study aims to build an end-to-end analytics solution that includes both real-time machine condition monitoring and machine learning based predictive analytics capabilities. The goal is to showcase a general predictive maintenance solution architecture, which suggests how the data generated from field machines can be collected, transmitted, stored, and analyzed. We use a publicly available aircraft engine run-to-failure dataset to illustrate the streaming analytics component and the batch failure prediction component. We outline the contributions of this study from four aspects. First, we compare the predictive maintenance problems from the view of the traditional reliability centered maintenance field, and from the view of the IoT applications. When evolving to the IoT era, predictive maintenance has shifted its focus from ensuring reliable machine operations to improve production/maintenance efficiency via any maintenance related tasks. It covers a variety of topics, including but not limited to: failure prediction, fault forecasting, failure detection and diagnosis, and recommendation of maintenance actions after failure. Second, we review the state-of-art technologies that enable a machine/device to transmit data all the way through the Cloud for storage and advanced analytics. These technologies vary drastically mainly based on the power source and functionality of the devices. For example, a consumer machine such as an elevator uses completely different data transmission protocols comparing to the sensor units in an environmental sensor network. The former may transfer data into the Cloud via WiFi directly. The latter usually uses radio communication inherent the network, and the data is stored in a staging data node before it can be transmitted into the Cloud when necessary. Third, we illustrate show to formulate a machine learning problem to predict machine fault/failures. By showing a step-by-step process of data labeling, feature engineering, model construction and evaluation, we share following experiences: (1) what are the specific data quality issues that have crucial impact on predictive maintenance use cases; (2) how to train and evaluate a model when training data contains inter-dependent records. Four, we review the tools available to build such a data pipeline that digests the data and produce insights. We show the tools we use including data injection, streaming data processing, machine learning model training, and the tool that coordinates/schedules different jobs. In addition, we show the visualization tool that creates rich data visualizations for both real-time insights and prediction results. To conclude, there are two key takeaways from this study. (1) It summarizes the landscape and challenges of predictive maintenance applications. (2) It takes an example in aerospace with publicly available data to illustrate each component in the proposed data pipeline and showcases how the solution can be deployed as a live demo.

Keywords: Internet of Things, machine learning, predictive maintenance, streaming data

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Authors: Tassadit Issaadi Hamitouche, Nicolas Gillard, Jean Petit, Valerie Lavaste, Celine Mayousse

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Water is a fundamental resource for the industry. It is taken from the environment either from municipal distribution networks or from various natural water sources such as the sea, ocean, rivers, aquifers, etc. Once used, water is discharged into the environment, reprocessed at the plant or treatment plants. These withdrawals and discharges have a direct impact on natural water resources. These impacts can apply to the quantity of water available, the quality of the water used, or to impacts that are more complex to measure and less direct, such as the health of the population downstream from the watercourse, for example. Based on the analysis of data (meteorological, river characteristics, physicochemical substances), we wish to predict water stress episodes and anticipate prefectoral decrees, which can impact the performance of plants and propose improvement solutions, help industrialists in their choice of location for a new plant, visualize possible interactions between companies to optimize exchanges and encourage the pooling of water treatment solutions, and set up circular economies around the issue of water. The development of a system for the collection, processing, and use of data related to water resources requires the functional constraints specific to the latter to be made explicit. Thus the system will have to be able to store a large amount of data from sensors (which is the main type of data in plants and their environment). In addition, manufacturers need to have 'near-real-time' processing of information in order to be able to make the best decisions (to be rapidly notified of an event that would have a significant impact on water resources). Finally, the visualization of data must be adapted to its temporal and geographical dimensions. In this study, we set up an infrastructure centered on the TICK application stack (for Telegraf, InfluxDB, Chronograf, and Kapacitor), which is a set of loosely coupled but tightly integrated open source projects designed to manage huge amounts of time-stamped information. The software architecture is coupled with the cross-industry standard process for data mining (CRISP-DM) data mining methodology. The robust architecture and the methodology used have demonstrated their effectiveness on the study case of learning the level of a river with a 7-day horizon. The management of water and the activities within the plants -which depend on this resource- should be considerably improved thanks, on the one hand, to the learning that allows the anticipation of periods of water stress, and on the other hand, to the information system that is able to warn decision-makers with alerts created from the formalization of prefectoral decrees.

Keywords: data mining, industry, machine Learning, shortage, water resources

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Authors: Amir Gerayeli, Babak Moradi

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The continuously declining oil reservoirs and reservoirs aging have created a huge demand for utilization of Enhanced Oil Recovery (EOR) methods recently. Primary and secondary oil recovery methods have various limitations and are not practical for all reservoirs. Therefore, it is necessary to use chemical methods to improve oil recovery efficiency by reducing oil and water surface tension, increasing sweeping efficiency, and reducing displacer phase viscosity. One of the well-known methods of oil recovery is Alkaline-Surfactant-Polymer (ASP) flooding that shown to have significant impact on enhancing oil recovery. As some of the biggest oil reservoirs including those of Iran’s are fractional reservoirs with substantial amount of trapped oil in their fractures, the use of Alkaline-Surfactant-Polymer (ASP) flooding method is increasingly growing, the method in which the impact of several parameters including type and concentration of the Alkaline, Surfactant, and polymer are particularly important. This study investigated the use of Nano particles to improve Enhanced Oil Recovery (EOR). The study methodology included performing several laboratory tests on drill cores extracted from Karanj Oil field Asmary Formation in Khuzestan, Iran. In the experiments performed, Sodium dodecyl benzenesulfonate (SDBS) and 1-dodecyl-3-methylimidazolium chloride ([C12mim] [Cl])) were used as surfactant, hydrolyzed polyacrylamide (HPAM) and guar gum were used as polymer, Sodium hydroxide (NaOH) as alkaline, and Silicon dioxide (SiO2) and Magnesium oxide (MgO) were used as Nano particles. The experiment findings suggest that water viscosity increased from 1 centipoise to 5 centipoise when hydrolyzed polyacrylamide (HPAM) and guar gum were used as polymer. The surface tension between oil and water was initially measured as 25.808 (mN/m). The optimum surfactant concentration was found to be 500 p, at which the oil and water tension surface was measured to be 2.90 (mN/m) when [C12mim] [Cl] was used, and 3.28 (mN/m) when SDBS was used. The Nano particles concentration ranged from 100 ppm to 1500 ppm in this study. The optimum Nano particle concentration was found to be 1000 ppm for MgO and 500 ppm for SiO2.

Keywords: alkaline-surfactant-polymer, ionic liquids, relative permeability, reduced surface tension, tertiary enhanced oil recovery, wettability change

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Authors: Manoel Pereira, Alvaro Veiga, Camila Epprecht, Renato Costa

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This paper introduces an empirical version of the Esscher transform for risk-neutral option pricing. Traditional parametric methods require the formulation of an explicit risk-neutral model and are operational only for a few probability distributions for the returns of the underlying. In our proposal, we make only mild assumptions on the pricing kernel and there is no need for the formulation of the risk-neutral model for the returns. First, we simulate sample paths for the returns under the physical distribution. Then, based on the empirical Esscher transform, the sample is reweighted, giving rise to a risk-neutralized sample from which derivative prices can be obtained by a weighted sum of the options pay-offs in each path. We compare our proposal with some traditional parametric pricing methods in four experiments with artificial and real data.

Keywords: esscher transform, generalized autoregressive Conditional Heteroscedastic (GARCH), nonparametric option pricing

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Authors: Nicole Merkle, Stefan Zander

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Ambient Assisted Living (AAL) describes a technological and methodological stack of (e.g. formal model-theoretic semantics, rule-based reasoning and machine learning), different aspects regarding the behavior, activities and characteristics of humans. Hence, a semantic representation of the user environment and its relevant elements are required in order to allow assistive agents to recognize situations and deduce appropriate actions. Furthermore, the user and his/her characteristics (e.g. physical, cognitive, preferences) need to be represented with a high degree of expressiveness in order to allow software agents a precise evaluation of the users’ context models. The correct interpretation of these context models highly depends on temporal, spatial circumstances as well as individual user preferences. In most AAL approaches, model representations of real world situations represent the current state of a universe of discourse at a given point in time by neglecting transitions between a set of states. However, the AAL domain currently lacks sufficient approaches that contemplate on the dynamic adaptions of context-related representations. Semantic representations of relevant real-world excerpts (e.g. user activities) help cognitive, rule-based agents to reason and make decisions in order to help users in appropriate tasks and situations. Furthermore, rules and reasoning on semantic models are not sufficient for handling uncertainty and fuzzy situations. A certain situation can require different (re-)actions in order to achieve the best results with respect to the user and his/her needs. But what is the best result? To answer this question, we need to consider that every smart agent requires to achieve an objective, but this objective is mostly defined by domain experts who can also fail in their estimation of what is desired by the user and what not. Hence, a smart agent has to be able to learn from context history data and estimate or predict what is most likely in certain contexts. Furthermore, different agents with contrary objectives can cause collisions as their actions influence the user’s context and constituting conditions in unintended or uncontrolled ways. We present an approach for dynamically updating a semantic model with respect to the current user context that allows flexibility of the software agents and enhances their conformance in order to improve the user experience. The presented approach adapts rules by learning sensor evidence and user actions using probabilistic reasoning approaches, based on given expert knowledge. The semantic domain model consists basically of device-, service- and user profile representations. In this paper, we present how this semantic domain model can be used in order to compute the probability of matching rules and actions. We apply this probability estimation to compare the current domain model representation with the computed one in order to adapt the formal semantic representation. Our approach aims at minimizing the likelihood of unintended interferences in order to eliminate conflicts and unpredictable side-effects by updating pre-defined expert knowledge according to the most probable context representation. This enables agents to adapt to dynamic changes in the environment which enhances the provision of adequate assistance and affects positively the user satisfaction.

Keywords: ambient intelligence, machine learning, semantic web, software agents

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Authors: Chiara Cesali

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The effects of climate change are increasingly evident: increases in temperature (i.e. global warming), greater frequency of extreme weather events, i.e. storms, floods, which often affect transport infrastructures. Large-scale climatological models with long-term horizons (up to 2100) show the possibility of significant increases in precipitation in the future, according to the greenhouse gas emissions scenarios from IPCC. Consequently, the insufficiency of existing hydraulic works (i.e. bridges, culverts, drainage systems) may be more frequent, or those currently being designed may become insufficient in the future. Thus, the hydraulic design methods of transport infrastructure must begin to take into account the influence of climate change. To this purpose, criteria for applying to the hydraulic design of a railway infrastructure some of the approaches currently available for determining design rainfall intensity and/or peak discharge flow on the basis of possible climate change scenarios are defined and proposed in the paper. Some application cases are also described.

Keywords: climate change, hydraulic design, precipitation, railway

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Authors: Tianqi Wu, Min Wang

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Research investigating language acquisition from a constructionist perspective has demonstrated that language is learned as constructions at various linguistic levels, which is related to factors of frequency, semantic prototypicality, and form-meaning contingency. However, previous research on construction learning tended to focus on clause-level constructions such as verb argument constructions but few attempts were made to study morpheme-level constructions such as the progressive construction, which is regarded as a source of acquisition problems for English learners from diverse L1 backgrounds, especially for those whose L1 do not have an equivalent construction such as German and Chinese. To trace the developmental trajectory of Chinese EFL learners’ use of the progressive with respect to verb frequency, verb-progressive contingency, and verbal prototypicality and generality, a learner corpus consisting of three sub-corpora representing three different English proficiency levels was extracted from the Chinese Learners of English Corpora (CLEC). As the reference point, a native speakers’ corpus extracted from the Louvain Corpus of Native English Essays was also established. All the texts were annotated with C7 tagset by part-of-speech tagging software. After annotation all valid progressive hits were retrieved with AntConc 3.4.3 followed by a manual check. Frequency-related data showed that from the lowest to the highest proficiency level, (1) the type token ratio increased steadily from 23.5% to 35.6%, getting closer to 36.4% in the native speakers’ corpus, indicating a wider use of verbs in the progressive; (2) the normalized entropy value rose from 0.776 to 0.876, working towards the target score of 0.886 in native speakers’ corpus, revealing that upper-intermediate learners exhibited a more even distribution and more productive use of verbs in the progressive; (3) activity verbs (i.e., verbs with prototypical progressive meanings like running and singing) dropped from 59% to 34% but non-prototypical verbs such as state verbs (e.g., being and living) and achievement verbs (e.g., dying and finishing) were increasingly used in the progressive. Apart from raw frequency analyses, collostructional analyses were conducted to quantify verb-progressive contingency and to determine what verbs were distinctively associated with the progressive construction. Results were in line with raw frequency findings, which showed that contingency between the progressive and non-prototypical verbs represented by light verbs (e.g., going, doing, making, and coming) increased as English proficiency proceeded. These findings altogether suggested that beginning Chinese EFL learners were less productive in using the progressive construction: they were constrained by a small set of verbs which had concrete and typical progressive meanings (e.g., the activity verbs). But with English proficiency increasing, their use of the progressive began to spread to marginal members such as the light verbs.

Keywords: Construction learning, Corpus-based, Progressives, Prototype

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Authors: Bernard Igoche Igoche, Olumuyiwa Matthew, Peter Bednar, Alexander Gegov

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This study presents an ontology-driven approach for knowledge discovery and validation from admission databases in Nigerian polytechnic institutions. The research aims to address the challenges of extracting meaningful insights from vast amounts of admission data and utilizing them for decision-making and process improvement. The proposed methodology combines the knowledge discovery in databases (KDD) process with a structural causal model (SCM) ontological framework. The admission database of Benue State Polytechnic Ugbokolo (Benpoly) is used as a case study. The KDD process is employed to mine and distill knowledge from the database, while the SCM ontology is designed to identify and validate the important features of the admission process. The SCM validation is performed using the conditional independence test (CIT) criteria, and an algorithm is developed to implement the validation process. The identified features are then used for machine learning (ML) modeling and prediction of admission status. The results demonstrate the adequacy of the SCM ontological framework in representing the admission process and the high predictive accuracies achieved by the ML models, with k-nearest neighbors (KNN) and support vector machine (SVM) achieving 92% accuracy. The study concludes that the proposed ontology-driven approach contributes to the advancement of educational data mining and provides a foundation for future research in this domain.

Keywords: admission databases, educational data mining, machine learning, ontology-driven knowledge discovery, polytechnic education, structural causal model

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Authors: Noriyuki Suyama

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The progress and spread of digitalization have led to the provision of a variety of new services. The recent progress in digitization can be attributed to rapid developments in science and technology. First, the research and diffusion of artificial intelligence (AI) has made dramatic progress. Around 2000, the third wave of AI research, which had been underway for about 50 years, arrived. Specifically, machine learning and deep learning were made possible in AI, and the ability of AI to acquire knowledge, define the knowledge, and update its own knowledge in a quantitative manner made the use of big data practical even for commercial PCs. On the other hand, with the spread of social media, information exchange has become more common in our daily lives, and the lending and borrowing of goods and services, in other words, the sharing economy, has become widespread. The scope of this trend is not limited to any industry, and its momentum is growing as the SDGs take root. In addition, the Social Network Service (SNS), a part of social media, has brought about the evolution of the retail business. In the past few years, social network services (SNS) involving users or companies have especially flourished. The People's Republic of China (hereinafter referred to as "China") is a country that is stimulating enormous consumption through its own unique SNS, which is different from the SNS used in developed countries around the world. This paper focuses on the effectiveness and challenges of influencer marketing by focusing on the influence of influencers on users' behavior and satisfaction with Chinese SNSs. Specifically, Conducted was the quantitative survey of Tik Tok users living in China, with the aim of gaining new insights from the analysis and discussions. As a result, we found several important findings and knowledge.

Keywords: customer satisfaction, social networking services, influencer marketing, Chinese consumers’ behavior

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Authors: W. Soufi, F. Boukli Hacene, S. Ghalem

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Alzheimer's disease (AD) is a neurodegenerative disease that leads to a progressive and permanent deterioration of nerve cells. This disease is progressively accompanied by an intellectual deterioration leading to psychological manifestations and behavioral disorders that lead to a loss of autonomy. It is the most frequent of degenerative dementia. Alzheimer's disease (AD), which affects a growing number of people, has become a major public health problem in a few years. In the context of the study of the mechanisms governing the evolution of AD disease, we have found that natural flavonoids are good acetylcholinesterase inhibitors that reduce the rate of ßA secretion in neurons. This work is to study the inhibition of acetylcholinesterase (AChE) which is an enzyme involved in Alzheimer's disease, by methods of molecular modeling. These results will probably help in the development of an effective therapeutic tool in the fight against the development of Alzheimer's disease. Our goal of the research is to study the inhibition of acetylcholinesterase (AChE) by molecular modeling methods.

Keywords: Alzheimer's disease, acetylcholinesterase, flavonoids, molecular modeling

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Authors: S. Oudjemia, F. Alim, S. Seddiki

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This paper shows the application of multifractal analysis for additional help in cancer diagnosis. The medical image processing is a very important discipline in which many existing methods are in search of solutions to real problems of medicine. In this work, we present results of multifractal analysis of brain MRI images. The purpose of this analysis was to separate between healthy and cancerous tissue of the brain. A nonlinear method based on multifractal detrending moving average (MFDMA) which is a generalization of the detrending fluctuations analysis (DFA) is used for the detection of abnormalities in these images. The proposed method could make separation of the two types of brain tissue with success. It is very important to note that the choice of this non-linear method is due to the complexity and irregularity of tumor tissue that linear and classical nonlinear methods seem difficult to characterize completely. In order to show the performance of this method, we compared its results with those of the conventional method box-counting.

Keywords: irregularity, nonlinearity, MRI brain images, multifractal analysis, brain tumor

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Authors: Z. V. P. Murthy, Preeti Arunachalam, Sangeeta Balram

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Removal of metal ions from different wastewaters has become important due to their effects on living beings. Cadmium is one of the heavy metals found in different industrial wastewaters. There are many conventional methods available to remove heavy metals from wastewaters like adsorption, membrane separations, precipitation, electrolytic methods, etc. and all of them have their own advantages and disadvantages. The present work deals with the use of natural biosorbents (chitin and chitosan) to separate cadmium ions from aqueous solutions. The adsorption data were fitted with different isotherms and kinetics models. Amongst different adsorption isotherms used to fit the adsorption data, the Freundlich isotherm showed better fits for both the biosorbents. The kinetics data of adsorption of cadmium showed better fit with pseudo-second order model for both the biosorbents. Chitosan, the derivative from chitin, showed better performance than chitin. The separation results are encouraging.

Keywords: chitin, chitosan, cadmium, isotherm, kinetics

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Authors: Carol S. Binnekade, Deon Scott, Christina C. Shuttleworth, Annelien A. Van Rooyen

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Higher education institutions are constantly challenged to deliver skilled graduates into the workplace. Employers expect graduates to have the required technical knowledge as well as various pervasive skills. This also applies to accountants who need to know the technical requirements of financial reporting and be able to communicate with individuals, teams and clients at a high level. Accountants need to develop effective business conversational skills and use these skills to communicate up, down and across organizations, taking into consideration cultural and gender diversity. In addition, they need to master business writing and presentation skills. However, providing students with these skills in a distance-learning environment where interaction between students and instructors is limited, is a challenge for academics. The study on which this paper reports, forms part of a larger body of research, which explored the perceptions of accounting practitioners of the communication skills (or lack thereof) of recently qualified accounting students. Feedback (qualitative and quantitative) was obtained from various accounting practitioners in South Africa. Taking into consideration that distance learners communicate mainly with their instructors via email communication and their assignments are submitted using various word processor software, the researchers were of the opinion that the accounting graduates would be capable of communicating effectively once they entered the workplace. However, the research findings, inter alia, suggested that the accounting graduates lacked communication skills and that training was needed to differentiate between business and social communication once they entered the workplace. Recommendations on how these communication challenges may be addressed by higher education institutions are provided.

Keywords: accounting practitioners, communication skills, distance education, pervasive skills

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Authors: Alvin Uchenna Ugwu

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The United Nation’s 2030 agenda and Global Action Programme (GAP) for implementation of the Sustainable Development Goals (SDGs), has mandated all sectors in the societies, including education, to develop strategies towards actualizing sustainability in all facets of the society, by the year 2030. Education is no doubt a key tool for social change. However, educational institutions in most African nations need a paradigmatic shift to strike a balance between policies (curricular) and practices, with regards to Education for Sustainable Development (ESD). The paradigm shift in this regard is described as whole-institution/school approach. The whole institution approaches advocate action-focused ESD. In other words, ESD policy and curriculum makers, formal and non-formal education institutions, need to ‘practice what they preach’. This paper is developed from an ongoing study carried out by the author and guided by two research questions: -What are the views of intermediate phase science and technology preservice teachers on the ESD content included in the science and technology modules? -What challenges or enable intermediate phase science and technology pre-service teachers to learn about ESD in science and technology modules? The study drew from the views and experiences of preservice science teachers, learning about ESD in a university’s college of education in South Africa. Using qualitative case study research design, the research data were generated via questionnaires and focus group discussions. Analysis of generated data indicates that universities and institutions of higher learning need to demonstrate practical involvement while implementing ESD in societies, rather than just standing as knowledge media. Findings of the study further suggest that natural sciences and technology courses in teacher education programmes and other institutions of higher learning, should be perceived as key transformative tools in shaping the consciousness of students towards integrating and fostering ESD in developing countries such as South Africa. Thus, this paper seeks to promote ‘Whole Institution Involvement’ in teacher education colleges in South Africa, as a measure of improving ESD in higher education settings. The paper suggests that in order to achieve ESD in higher education settings and beyond, policies and practices should be reexamined beyond rhetoric and buzzwords. The paper further argues that implementation of ESD is largely influenced by context, hence two different contexts should be examined empirically.

Keywords: education for sustainable development, higher education institutions, pre-service science teachers, qualitative case study research, whole institution involvement

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Authors: Tamar Lazariashvili

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There are a number of factors that determine the growth and development of the country's economy; however, trade and economic relations with other countries are the most important of all these factors. The paper analyzes the trade and economic relations between Georgia and Germany, identifies the impediments caused by the Covid pandemic, and substantiates the need for further economic cooperation between the countries. Research objectives. The objective of the research is to develop recommendations and reveal the prospects of further cooperation between Georgia and Germany based on identifying the problems in the field of trade and economy in the post-crisis situation. The research object is Georgian German economic relations. Germany is Georgia's largest trading partner in the European Union. Georgia and Germany actively cooperate within the framework of international organizations as well. The paper analyzes the multilateral and intensive economic relations between Germany and Georgia; evaluates the investments of German companies in Georgia and the activities of Georgian companies in Germany. Research methods. The paper uses general and specific research methods; in particular, analysis, synthesis, induction, deduction, comparison, statistical (selection, grouping, observation, trend), and other research methods.SWOT analysis is used to determine development opportunities between countries. As a result of the research economic ranking of Georgia and Germany are determined according to the above criteria, the causes of the impediments due to the pandemic are studied; the main problems in the field of trade and economy are identified. The paper provides conclusions on the problems in the trade relations between Georgia and Germany and suggests recommendations regarding the prospects for improving these relations.

Keywords: georgia-germany, trade and economic relations, economic ranking, perspective directions

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Authors: I. Erins, J. Erina

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The aim of the present article is to determine the impact of the external and internal factors of bank performance on the profitability indicators of the CEE countries banks in the period from 2006 to 2012. On the basis of research conducted abroad on bank and macroeconomic profitability indicators, in order to obtain research results, the authors evaluated return on average assets (ROAA) and return on average equity (ROAE) indicators of the CEE countries banks. The authors analyzed profitability indicators of banks using descriptive methods, SPSS data analysis methods as well as data correlation and linear regression analysis. The authors concluded that most internal and external indicators of bank performance have no direct effect on the profitability of the banks in the CEE countries. The only exceptions are credit risk and bank size which affect one of the measures of bank profitability–return on average equity.

Keywords: banks, CEE countries, profitability ROAA, ROAE

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Authors: Nor Sahida Ibrahim, Ruhaila Maskat, Aishah Ahmad

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Almost all tertiary-level students will undergo some form of training in organisations prior to their graduation. This practice provides the necessary exposure and experience to allow students to cope with actual working environment and culture in the future. Nevertheless, a particular degree of “matching” between what is expected and what can be offered between students and organisations underpins how effective and enriching the experience is. This matching of students and organisations is challenging when preferences from both parties must be satisfied. This work developed a web-based system, namely the OrganisationMatcher, which leverage on the use of preference weights to score each organisation and rank them based on “suitability”. OrganisationMatcher has been implemented on a relational database, designed using object-oriented methods and developed using PHP programming language for browser front-end access. We outline the challenges and limitations of our system and discuss future improvements to the system, specifically in the utilisation of intelligent methods.

Keywords: student industrial placement, information system, web-based, ranking

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Authors: Ramune Kasperaviciene, Jurgita Motiejuniene, Dalia Venckiene

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Familiarising students with strategies for transferring cultural meanings (intertextual units, culture-specific idioms, culture-specific items, etc.) should be part of a comprehensive translator training programme. The present paper focuses on strategies for transferring such meanings into other languages and explores possibilities for introducing these methods and practice to translation students. The authors (university translation teachers) analyse the means of transferring cultural meanings from English into Lithuanian in a specific travel book, attribute these means to theoretically grounded strategies, and make calculations related to the frequency of adoption of specific strategies; translation students are familiarised with concepts and methods related to transferring cultural meanings and asked to put their theoretical knowledge into practice, i.e. interpret and translate certain culture-specific items from the same source text, and ground their decisions on theory; the comparison of the strategies employed by the professional translator of the source text (as identified by the authors of this study) and by the students is made. As a result, both students and teachers gain valuable experience, and new practices of conducting translation classes for a specific purpose evolve. Conclusions highlight the differences and similarities of non-professional and professional choices, summarise the possibilities for introducing methods of transferring cultural meanings to students, and round up with specific considerations of the impact of theoretical knowledge and the degree of experience on decisions made in the translation process.

Keywords: cultural meanings, culture-specific items, strategies for transferring cultural meanings, translator training

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