Search results for: Learning materials
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 13259

Search results for: Learning materials

10199 Levels of Reflection in Engineers EFL Learners: The Path to Content and Language Integrated Learning Implementation in Chilean Higher Education

Authors: Sebastián Olivares Lizana, Marianna Oyanedel González

Abstract:

This study takes part of a major project based on implementing a CLIL program (Content and Language Integrated Learning) at Universidad Técnica Federico Santa María, a leading Chilean tertiary Institution. It aims at examining the relationship between the development of Reflective Processes (RP) and Cognitive Academic Language Proficiency (CALP) in weekly learning logs written by faculty members, participants of an initial professional development online course on English for Academic Purposes (EAP). Such course was designed with a genre-based approach, and consists of multiple tasks directed to academic writing proficiency. The results of this analysis will be described and classified in a scale of key indicators that represent both the Reflective Processes and the advances in CALP, and that also consider linguistic proficiency and task progression. Such indicators will evidence affordances and constrains of using a genre-based approach in an EFL Engineering CLIL program implementation at tertiary level in Chile, and will serve as the starting point to the design of a professional development course directed to teaching methodologies in a CLIL EFL environment in Engineering education at Universidad Técnica Federico Santa María.

Keywords: EFL, EAL, genre, CLIL, engineering

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10198 Unbranched, Saturated, Carboxylic Esters as Phase-Change Materials

Authors: Anastasia Stamatiou, Melissa Obermeyer, Ludger J. Fischer, Philipp Schuetz, Jörg Worlitschek

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This study evaluates unbranched, saturated carboxylic esters with respect to their suitability to be used as storage media for latent heat storage applications. Important thermophysical properties are gathered both by means of literature research as well as by experimental measurements. Additionally, esters are critically evaluated against other common phase-change materials in terms of their environmental impact and their economic potential. The experimental investigations are performed for eleven selected ester samples with a focus on the determination of their melting temperature and their enthalpy of fusion using differential scanning calorimetry. Transient Hot Bridge was used to determine the thermal conductivity of the liquid samples while thermogravimetric analysis was employed for the evaluation of the 5% weight loss temperature as well as of the decomposition temperature of the non-volatile samples. Both experimental results and literature data reveal the high potential of esters as phase-change materials. Their good thermal and environmental properties as well as the possibility for production from natural sources (e.g. vegetable oils) render esters as very promising for future storage applications. A particularly high short term application potential of esters could lie in low temperature storage applications where the main alternative is using salt hydrates as phase-change material.

Keywords: esters, phase-change materials, thermal properties, latent heat storage

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10197 Intensive Intercultural English Language Pedagogy among Parents from Culturally and Linguistically Diverse Backgrounds (CALD)

Authors: Ann Dashwood

Abstract:

Using Standard Australian English with confidence is a cultural expectation of parents of primary school aged children who want to engage effectively with their children’s teachers and school administration. That confidence in support of their children’s learning at school is seldom experienced by parents whose first language is not English. Sharing language with competence in an intercultural environment is the common denominator for meaningful communication and engagement to occur in a school community. Experience in relevant, interactive sessions is known to enhance engagement and participation. The purpose of this paper is to identify a pedagogy for parents otherwise isolated from daily use of functional Australian cultural language learned to engage effectively in their children’s learning at school. The outcomes measure parents’ intercultural engagement with classroom teachers and attention to the school’s administrative procedures using quantitative and qualitative methods. A principled communicative task-based language learning approach, combined with intercultural communication strategies provide the theoretical base for intensive English inquiry-based learning and engagement. The quantitative analysis examines data samples collected by classroom teachers and administrators and parents’ writing samples. Interviews and observations qualitatively inform the study. Currently, significant numbers of projects are active in community centers and schools to enhance English language knowledge of parents from Language Backgrounds Other Than English (LBOTE). The study is significant to explore the effects of an intensive English pedagogy with parents of varied English language backgrounds, by targeting inquiry-based language use for social interactions in the school and wider community, specific engagement and cultural interaction with teachers and school activities and procedures.

Keywords: engagement, intercultural communication, language teaching pedagogy, LBOTE, school community

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10196 The Fabric of Culture: Deciphering the Discourse of Permitted and Prohibited Raw Materials for Clothing in Hadith Literature

Authors: Hadas Hirsch

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Clothing is aimed at concealing and revealing the body, protecting it, and manifesting religious, political, and social declarations. The material and symbolic meanings of clothing and its raw materials are evaluated through the context of their social, cultural, and religious systems. The raw materials for clothing that were frequent and familiar in the 7th century Arab Peninsula were wool, leather, cotton, and some kinds of silk. The spread of the Muslim empire and the intersections with other religions and cultures enable the trickling of new raw materials that were unknown to Muslims or unaccepted. The sources for this research are hadith collections that discuss in details various kinds of textiles and their origin, together with a legal explanation that permits or prohibits its use. The paper will describe and analyze this discussion by contextualizing it in social, religious, and cultural reality that creates a structure of socio-religious dependency. The aim is not to identify, catalogue, and technically analyze fabrics but to reveal their role in Muslims’ life as a means of creating dependency for the community and setting borders inside and outside. The analysis is built upon a scale that starts with the most recommended raw materials, then comes the permitted ones and, in the end, the prohibited raw materials. This mapping will provide an insight into the ways textiles, as a cultural medium, help to shape and redefine identities and, at the same time, enable a sphere for creative expression within socio-cultural and religious limits and context. To sum up, hadith literature has the main role is characterizing Muslim clothing, from garments to textiles and colors, including multiple variations and contradicting aspects. The Muslim style of clothing and, in particular, textiles is a manifestation of the socio-religious structure of dependency that creates differentiated Muslim identity together with subdivision of gendered groups. Some other aspects are the tension between authenticity and imitation and the jurists’ pragmatic and practice attitude that enables an individual sphere of expression within the limits of jurisprudence.

Keywords: Hadith, jurisprudence, medieval Islam, material culture

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10195 Application of Digital Tools for Improving Learning

Authors: José L. Jiménez

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The use of technology in the classroom is an issue that is constantly evolving. Digital age students learn differently than their teachers did, so now the teacher should be constantly evolving their methods and teaching techniques to be more in touch with the student. In this paper a case study presents how were used some of these technologies by accompanying a classroom course, this in order to provide students with a different and innovative experience as their teacher usually presented the activities to develop. As students worked in the various activities, they increased their digital skills by employing unknown tools that helped them in their professional training. The twenty-first century teacher should consider the use of Information and Communication Technologies in the classroom thinking in skills that students of the digital age should possess. It also takes a brief look at the history of distance education and it is also highlighted the importance of integrating technology as part of the student's training.

Keywords: digital tools, on-line learning, social networks, technology

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10194 Hand Gesture Interpretation Using Sensing Glove Integrated with Machine Learning Algorithms

Authors: Aqsa Ali, Aleem Mushtaq, Attaullah Memon, Monna

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In this paper, we present a low cost design for a smart glove that can perform sign language recognition to assist the speech impaired people. Specifically, we have designed and developed an Assistive Hand Gesture Interpreter that recognizes hand movements relevant to the American Sign Language (ASL) and translates them into text for display on a Thin-Film-Transistor Liquid Crystal Display (TFT LCD) screen as well as synthetic speech. Linear Bayes Classifiers and Multilayer Neural Networks have been used to classify 11 feature vectors obtained from the sensors on the glove into one of the 27 ASL alphabets and a predefined gesture for space. Three types of features are used; bending using six bend sensors, orientation in three dimensions using accelerometers and contacts at vital points using contact sensors. To gauge the performance of the presented design, the training database was prepared using five volunteers. The accuracy of the current version on the prepared dataset was found to be up to 99.3% for target user. The solution combines electronics, e-textile technology, sensor technology, embedded system and machine learning techniques to build a low cost wearable glove that is scrupulous, elegant and portable.

Keywords: American sign language, assistive hand gesture interpreter, human-machine interface, machine learning, sensing glove

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10193 The Use of Plant-Based Natural Fibers in Reinforced Cement Composites

Authors: N. AlShaya, R. Alhomidan, S. Alromizan, W. Labib

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Plant-based natural fibers are used more increasingly in construction materials. It is done to reduce the pressure on the built environment, which has been increased dramatically due to the increases world population and their needs. Plant-based natural fibers are abundant in many countries. Despite the low-cost of such environmental friendly renewable material, it has the ability to enhance the mechanical properties of construction materials. This paper presents an extensive discussion on the use of plant-based natural fibers as reinforcement for cement-based composites, with a particular emphasis upon fiber types; fiber characteristics, and fiber-cement composites performance. It also covers a thorough overview on the main factors, affecting the properties of plant-based natural fiber cement composite in it fresh and hardened state. The feasibility of using plant-based natural fibers in producing various construction materials; such as, mud bricks and blocks is investigated. In addition, other applications of using such fibers as internal curing agents as well as durability enhancer are also discussed. Finally, recommendation for possible future work in this area is presented.

Keywords: natural fibres, cement composites, construction materia, sustainability, stregth, durability

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10192 Student Perceptions on Administrative Support in the Delivering of Open Distance Learning Programmes – A Case Study

Authors: E. J. Spamer, J. M. Van Zyl, MHA Combrinck

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The Unit for Open Distance Learning (UODL) at the North-West University (NWU), South Africa was established in 2013 with its main function to deliver open distance learning (ODL) programmes to approximately 30 000 students from the Faculties of Education Sciences, Health Sciences, Theology and Arts and Culture. Quality operational and administrative processes are key components in the delivery of these programmes and they need to function optimally for students to be successful in their studies. Operational and administrative processes include aspects such as applications, registration, dissemination of study material, availability of electronic platforms, the management of assessment, and the dissemination of important information. To be able to ensure and enhance quality during these processes, it is vital to determine students’ perceptions with regards to these mentioned processes. A questionnaire was available online and also distributed to the 63 tuition centres. The purpose of this research was to determine the perceptions of ODL students from NWU regarding operational and administrative processes. 1903 students completed and submitted the questionnaire. The data was quantitatively analysed and discussed. Results indicated that the majority of students are satisfied with the operational and administrative processes; however, the results also indicated some areas that need improvement. The data gathered is important to identify strengths and areas for improvement and form part of a bigger strategy of qualitative assurance at the UODL.

Keywords: administrative support, ODL programmes, quantitative study, students' perceptions

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10191 The Impact of Neonatal Methamphetamine on Spatial Learning and Memory of Females in Adulthood

Authors: Ivana Hrebickova, Maria Sevcikova, Romana Slamberova

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The present study was aimed at evaluation of cognitive changes following scheduled neonatal methamphetamine exposure in combination with long-term exposure in adulthood of female Wistar rats. Pregnant mothers were divided into two groups: group with indirect exposure (methamphetamine in dose 5 mg/ml/kg, saline in dose 1 ml/kg) during early lactation period (postnatal day 1–11) - progeny of these mothers were exposed to the effects of methamphetamine or saline indirectly via the breast milk; and the second group with direct exposure – all mothers were left intact for the entire lactation period, while progeny was treated with methamphetamine (5 mg/ml/kg) by injection or the control group, which was received needle pick (shame, not saline) at the same time each day of period of application (postnatal day 1–11). Learning ability and memory consolidation were tested in the Morris Water Maze, which consisted of three types of tests: ‘Place Navigation Test ‘; ‘Probe Test ‘; and ‘Memory Recall Test ‘. Adult female progeny were injected daily, after completion last trial with saline or methamphetamine (1 mg/ml/kg). We compared the effects of indirect/direct neonatal methamphetamine exposure and adult methamphetamine treatment on cognitive function of female rats. Statistical analyses showed that neonatal methamphetamine exposure worsened spatial learning and ability to remember the position of the platform. The present study demonstrated that direct methamphetamine exposure has more significant impact on process of learning and memory than indirect exposure. Analyses of search strategies (thigmotaxis, scanning) used by females during the Place Navigation Test and Memory Recall Test confirm all these results.

Keywords: methamphetamine, Morris water maze, neonatal exposure, strategies, Wistar rats

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10190 Fraud Detection in Credit Cards with Machine Learning

Authors: Anjali Chouksey, Riya Nimje, Jahanvi Saraf

Abstract:

Online transactions have increased dramatically in this new ‘social-distancing’ era. With online transactions, Fraud in online payments has also increased significantly. Frauds are a significant problem in various industries like insurance companies, baking, etc. These frauds include leaking sensitive information related to the credit card, which can be easily misused. Due to the government also pushing online transactions, E-commerce is on a boom. But due to increasing frauds in online payments, these E-commerce industries are suffering a great loss of trust from their customers. These companies are finding credit card fraud to be a big problem. People have started using online payment options and thus are becoming easy targets of credit card fraud. In this research paper, we will be discussing machine learning algorithms. We have used a decision tree, XGBOOST, k-nearest neighbour, logistic-regression, random forest, and SVM on a dataset in which there are transactions done online mode using credit cards. We will test all these algorithms for detecting fraud cases using the confusion matrix, F1 score, and calculating the accuracy score for each model to identify which algorithm can be used in detecting frauds.

Keywords: machine learning, fraud detection, artificial intelligence, decision tree, k nearest neighbour, random forest, XGBOOST, logistic regression, support vector machine

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10189 Recovery of Waste: Feasibility and Sustainable Application of Residues from Drinking Water Treatment in Building Materials

Authors: Flavio Araujo, Julio Lima, Paulo Scalize, Antonio Albuquerque, Isabela Santos

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The aim of this study was to perform the physicochemical characterizations of the residue generated in the Meia-Ponte Water Treatment Plant, seeking to obtain normative parameters and consider sustainable alternatives for reincorporation of the residues in the productive chain for manufacturing various materials construction. In order to reduce the environmental liabilities generated by sanitation companies and discontinue unsustainable forms of disposal as the launching of the residue in the rivers, disposal in landfills or burning it, because such ways pollute watercourses, ground and air. The analyzes performed: Granulometry, identification of clay minerals, Scanning Electron Microscopy, and X-Ray Diffraction demonstrated the potential application of residues to replace the soil and sand, because it has characteristics compatible with small aggregate and can be used as feedstock for the manufacture of materials as ceramic and soil-cement bricks, mortars, interlocking floors and concrete artifacts.

Keywords: recovery of waste, residue, sustainable, water treatment plant, WTR

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10188 Design of Hybrid Auxetic Metamaterials for Enhanced Energy Absorption under Compression

Authors: Ercan Karadogan, Fatih Usta

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Auxetic materials have a negative Poisson’s ratio (NPR), which is not often found in nature. They are metamaterials that have potential applications in many engineering fields. Mechanical metamaterials are synthetically designed structures with unusual mechanical properties. These mechanical properties are dependent on the properties of the matrix structure. They have the following special characteristics, i.e., improved shear modulus, increased energy absorption, and intensive fracture toughness. Non-auxetic materials compress transversely when they are stretched. The system naturally is inclined to keep its density constant. The transversal compression increases the density to balance the loss in the longitudinal direction. This study proposes to improve the crushing performance of hybrid auxetic materials. The re-entrant honeycomb structure has been combined with a star honeycomb, an S-shaped unit cell, a double arrowhead, and a structurally hexagonal re-entrant honeycomb by 9 X 9 cells, i.e., the number of cells is 9 in the lateral direction and 9 in the vertical direction. The Finite Element (FE) and experimental methods have been used to determine the compression behavior of the developed hybrid auxetic structures. The FE models have been developed by using Abaqus software. The specimens made of polymer plastic materials have been 3D printed and subjected to compression loading. The results are compared in terms of specific energy absorption and strength. This paper describes the quasi-static crushing behavior of two types of hybrid lattice structures (auxetic + auxetic and auxetic + non-auxetic). The results show that the developed hybrid structures can be useful to control collapse mechanisms and present larger energy absorption compared to conventional re-entrant auxetic structures.

Keywords: auxetic materials, compressive behavior, metamaterials, negative Poisson’s ratio

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10187 Enhancement of Interface Properties of Thermoplastic Composite Materials

Authors: Reyhan Ozbask, Emek Moroydor Derin, Mustafa Dogu

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There are a limited number of global companies in the world that manufacture and commercially offer thermoplastic composite prepregs in accordance with aerospace requirements. High-performance thermoplastic materials supplied for aerospace structural applications are PEEK (polyetheretherketone), PPS (polyphenylsulfite), PEI (polyetherimide), and PEKK (polyetherketoneketone). Among these, PEEK is the raw material used in the first applications and has started to become widespread. However, the use of these thermoplastic raw materials in composite production is very difficult due to their high processing temperatures and impregnation difficulties. This study, it is aimed to develop carbon fiber-reinforced thermoplastic PEEK composites that comply with the requirements of the aviation industry that are superior mechanical properties as well as being lightweight. Therefore, it is aimed to obtain high-performance thermoplastic composite materials with improved interface properties by using the sizing method (suspension development through chemical synthesis and functionalization), to optimize the production process. The use of boron nitride nanotube as a bonding agent by modifying its surface constitutes the original aspect of the study as it has not been used in composite production with high-performance thermoplastic materials yet. For this purpose, laboratory-scale studies on the application of thermoplastic compatible sizing will be carried out in order to increase the fiber-matrix interfacial adhesion. The method respectively consists of the selection of appropriate sizing type, laboratory-scale carbon fiber (CF) / poly ether ether ketone (PEEK) polymer interface enhancement studies, manufacturing of laboratory-scale BNNT coated CF/PEEK woven prepreg composites and their tests.

Keywords: carbon fiber reinforced composite, interface enhancement, boron nitride nanotube, thermoplastic composite

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10186 Creative Experience and Revisit Intention of Handmade Oriental Parasol Umbrella in Kaohsiung

Authors: Yi-Ju Lee

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This study identified the hypothesised relationship between creative experience, and revisit intention of handmade oriental parasol umbrella in Kaohsiung, Taiwan. A face-to-face questionnaire survey was administered in Meinong town, Kaohsiung. The components of creative experience were found as “sense of achievement”, “unique learning” and “interaction with instructors” in creative tourism. The result also revealed significant positive relationships between creative experience and revisit intention in handmade activities. This paper provides additional suggestions for enhancing revisit intention and guidance regarding creative tourism.

Keywords: creative tourism, sense of achievement, unique learning, interaction with instructors, folk art

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10185 Using SMS Mobile Technology to Assess the Mastery of Subject Content Knowledge of Science and Mathematics Teachers of Secondary Schools in Tanzania

Authors: Joel S. Mtebe, Aron Kondoro, Mussa M. Kissaka, Elia Kibga

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Sub-Saharan Africa is described as the second fastest growing mobile phone penetration in the world more than in the United States or the European Union. Mobile phones have been used to provide a lot of opportunities to improve people’s lives in the region such as in banking, marketing, entertainment, and paying various bills such as water, TV, and electricity. However, the potential of using mobile phones to enhance teaching and learning has not been explored. This study presents an experience of developing and delivering SMS quizzes questions that were used to assess mastery of the subject content knowledge of science and mathematics secondary school teachers in Tanzania. The SMS quizzes were used as a follow up support mechanism to 500 teachers who participated in a project to upgrade subject content knowledge of science and mathematics subjects. Quizzes of 10-15 questions were sent to teachers each week for 8 weeks and the results were analyzed using SPSS. The results showed that chemistry and biology had better performance compared to mathematics and physics. Teachers reported some challenges that led to poor performance, invalid answers, and non-responses and they are presented. This research has several practical implications for those who are implementing or planning to use mobile phones for teaching and learning especially in rural secondary schools in sub-Saharan Africa.

Keywords: mobile learning, elearning, educational technolgies, SMS, secondary education, assessment

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10184 Evolving Convolutional Filter Using Genetic Algorithm for Image Classification

Authors: Rujia Chen, Ajit Narayanan

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Convolutional neural networks (CNN), as typically applied in deep learning, use layer-wise backpropagation (BP) to construct filters and kernels for feature extraction. Such filters are 2D or 3D groups of weights for constructing feature maps at subsequent layers of the CNN and are shared across the entire input. BP as a gradient descent algorithm has well-known problems of getting stuck at local optima. The use of genetic algorithms (GAs) for evolving weights between layers of standard artificial neural networks (ANNs) is a well-established area of neuroevolution. In particular, the use of crossover techniques when optimizing weights can help to overcome problems of local optima. However, the application of GAs for evolving the weights of filters and kernels in CNNs is not yet an established area of neuroevolution. In this paper, a GA-based filter development algorithm is proposed. The results of the proof-of-concept experiments described in this paper show the proposed GA algorithm can find filter weights through evolutionary techniques rather than BP learning. For some simple classification tasks like geometric shape recognition, the proposed algorithm can achieve 100% accuracy. The results for MNIST classification, while not as good as possible through standard filter learning through BP, show that filter and kernel evolution warrants further investigation as a new subarea of neuroevolution for deep architectures.

Keywords: neuroevolution, convolutional neural network, genetic algorithm, filters, kernels

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10183 Students' Experience Perception in Courses Taught in New Delivery Modes Compared to Traditional Modes

Authors: Alejandra Yanez, Teresa Benavides, Zita Lopez

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Even before COVID-19, one of the most important challenges that Higher Education faces today is the need for innovative educational methodologies and flexibility. We could all agree that one of the objectives of Higher Education is to provide students with a variety of intellectual and practical skills that, at the same time, will help them develop competitive advantages such as adaptation and critical thinking. Among the strategic objectives of Universidad de Monterrey (UDEM) has been to provide flexibility and satisfaction to students in the delivery modes of the academic offer. UDEM implemented a methodology that combines face to face with synchronous and asynchronous as delivery modes. UDEM goal, in this case, was to implement new technologies and different teaching methodologies that will improve the students learning experience. In this study, the experience of students during courses implemented in new delivery mode was compared with students in courses with traditional delivery modes. Students chose openly either way freely. After everything students around the world lived in 2020 and 2021, one can think that the face to face (traditional) delivery mode would be the one chosen by students. The results obtained in this study reveal that both delivery modes satisfy students and favor their learning process. We will show how the combination of delivery modes provides flexibility, so the proposal is that universities can include them in their academic offer as a response to the current student's learning interests and needs.

Keywords: flexibility, new delivery modes, student satisfaction, academic offer

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10182 A Long Short-Term Memory Based Deep Learning Model for Corporate Bond Price Predictions

Authors: Vikrant Gupta, Amrit Goswami

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The fixed income market forms the basis of the modern financial market. All other assets in financial markets derive their value from the bond market. Owing to its over-the-counter nature, corporate bonds have relatively less data publicly available and thus is researched upon far less compared to Equities. Bond price prediction is a complex financial time series forecasting problem and is considered very crucial in the domain of finance. The bond prices are highly volatile and full of noise which makes it very difficult for traditional statistical time-series models to capture the complexity in series patterns which leads to inefficient forecasts. To overcome the inefficiencies of statistical models, various machine learning techniques were initially used in the literature for more accurate forecasting of time-series. However, simple machine learning methods such as linear regression, support vectors, random forests fail to provide efficient results when tested on highly complex sequences such as stock prices and bond prices. hence to capture these intricate sequence patterns, various deep learning-based methodologies have been discussed in the literature. In this study, a recurrent neural network-based deep learning model using long short term networks for prediction of corporate bond prices has been discussed. Long Short Term networks (LSTM) have been widely used in the literature for various sequence learning tasks in various domains such as machine translation, speech recognition, etc. In recent years, various studies have discussed the effectiveness of LSTMs in forecasting complex time-series sequences and have shown promising results when compared to other methodologies. LSTMs are a special kind of recurrent neural networks which are capable of learning long term dependencies due to its memory function which traditional neural networks fail to capture. In this study, a simple LSTM, Stacked LSTM and a Masked LSTM based model has been discussed with respect to varying input sequences (three days, seven days and 14 days). In order to facilitate faster learning and to gradually decompose the complexity of bond price sequence, an Empirical Mode Decomposition (EMD) has been used, which has resulted in accuracy improvement of the standalone LSTM model. With a variety of Technical Indicators and EMD decomposed time series, Masked LSTM outperformed the other two counterparts in terms of prediction accuracy. To benchmark the proposed model, the results have been compared with traditional time series models (ARIMA), shallow neural networks and above discussed three different LSTM models. In summary, our results show that the use of LSTM models provide more accurate results and should be explored more within the asset management industry.

Keywords: bond prices, long short-term memory, time series forecasting, empirical mode decomposition

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10181 Using Gene Expression Programming in Learning Process of Rough Neural Networks

Authors: Sanaa Rashed Abdallah, Yasser F. Hassan

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The paper will introduce an approach where a rough sets, gene expression programming and rough neural networks are used cooperatively for learning and classification support. The Objective of gene expression programming rough neural networks (GEP-RNN) approach is to obtain new classified data with minimum error in training and testing process. Starting point of gene expression programming rough neural networks (GEP-RNN) approach is an information system and the output from this approach is a structure of rough neural networks which is including the weights and thresholds with minimum classification error.

Keywords: rough sets, gene expression programming, rough neural networks, classification

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10180 Neutralization of Sulphurous Waste (AMD) Using Recycled Waste Concrete

Authors: Ercument Koc, Banu Yaylali, Gulsen Tozsin, Haci Deveci

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Re-using of concrete waste materials for the neutralization of acid mine drainage (AMD) can protect the environment and contribute the national economy. The aim of this study was to investigate the prevention of AMD formation and heavy metal release using concrete wastes which are alkaline and generated by demolition of buildings within the urban renewal process. Shake flask test was conducted to determine the neutralization effects. Concrete wastes are rich in CaCO3 and they are used as a pH regulator for AMD neutralization. The results showed that pH of the AMD increased from 3.33 to 6.84 with the application of concrete waste materials.

Keywords: AMD, neutralization, sulphurous waste, urban renewal

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10179 An Audit of Climate Change and Sustainability Teaching in Medical School

Authors: Karolina Wieczorek, Zofia Przypaśniak

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Climate change is a rapidly growing threat to global health, and part of the responsibility to combat it lies within the healthcare sector itself, including adequate education of future medical professionals. To mitigate the consequences, the General Medical Council (GMC) has equipped medical schools with a list of outcomes regarding sustainability teaching. Students are expected to analyze the impact of the healthcare sector’s emissions on climate change. The delivery of the related teaching content is, however, often inadequate and insufficient time is devoted for exploration of the topics. Teaching curricula lack in-depth exploration of the learning objectives. This study aims to assess the extent and characteristics of climate change and sustainability subjects teaching in the curriculum of a chosen UK medical school (Barts and The London School of Medicine and Dentistry). It compares the data to the national average scores from the Climate Change and Sustainability Teaching (C.A.S.T.) in Medical Education Audit to draw conclusions about teaching on a regional level. This is a single-center audit of the timetabled sessions of teaching in the medical course. The study looked at the academic year 2020/2021 which included a review of all non-elective, core curriculum teaching materials including tutorials, lectures, written resources, and assignments in all five years of the undergraduate and graduate degrees, focusing only on mandatory teaching attended by all students (excluding elective modules). The topics covered were crosschecked with GMC Outcomes for graduates: “Educating for Sustainable Healthcare – Priority Learning Outcomes” as gold standard to look for coverage of the outcomes and gaps in teaching. Quantitative data was collected in form of time allocated for teaching as proxy of time spent per individual outcomes. The data was collected independently by two students (KW and ZP) who have received prior training and assessed two separate data sets to increase interrater reliability. In terms of coverage of learning outcomes, 12 out of 13 were taught (with the national average being 9.7). The school ranked sixth in the UK for time spent per topic and second in terms of overall coverage, meaning the school has a broad range of topics taught with some being explored in more detail than others. For the first outcome 4 out of 4 objectives covered (average 3.5) with 47 minutes spent per outcome (average 84 min), for the second objective 5 out of 5 covered (average 3.5) with 46 minutes spent (average 20), for the third 3 out of 4 (average 2.5) with 10 mins pent (average 19 min). A disproportionately large amount of time is spent delivering teaching regarding air pollution (respiratory illnesses), which resulted in the topic of sustainability in other specialties being excluded from teaching (musculoskeletal, ophthalmology, pediatrics, renal). Conclusions: Currently, there is no coherent strategy on national teaching of climate change topics and as a result an unstandardized amount of time spent on teaching and coverage of objectives can be observed.

Keywords: audit, climate change, sustainability, education

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10178 Field Trial of Resin-Based Composite Materials for the Treatment of Surface Collapses Associated with Former Shallow Coal Mining

Authors: Philip T. Broughton, Mark P. Bettney, Isla L. Smail

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Effective treatment of ground instability is essential when managing the impacts associated with historic mining. A field trial was undertaken by the Coal Authority to investigate the geotechnical performance and potential use of composite materials comprising resin and fill or stone to safely treat surface collapses, such as crown-holes, associated with shallow mining. Test pits were loosely filled with various granular fill materials. The fill material was injected with commercially available silicate and polyurethane resin foam products. In situ and laboratory testing was undertaken to assess the geotechnical properties of the resultant composite materials. The test pits were subsequently excavated to assess resin permeation. Drilling and resin injection was easiest through clean limestone fill materials. Recycled building waste fill material proved difficult to inject with resin; this material is thus considered unsuitable for use in resin composites. Incomplete resin permeation in several of the test pits created irregular ‘blocks’ of composite. Injected resin foams significantly improve the stiffness and resistance (strength) of the un-compacted fill material. The stiffness of the treated fill material appears to be a function of the stone particle size, its associated compaction characteristics (under loose tipping) and the proportion of resin foam matrix. The type of fill material is more critical than the type of resin to the geotechnical properties of the composite materials. Resin composites can effectively support typical design imposed loads. Compared to other traditional treatment options, such as cement grouting, the use of resin composites is potentially less disruptive, particularly for sites with limited access, and thus likely to achieve significant reinstatement cost savings. The use of resin composites is considered a suitable option for the future treatment of shallow mining collapses.

Keywords: composite material, ground improvement, mining legacy, resin

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10177 A Sense of Belonging: Music Learning and School Connectedness

Authors: Johanna Gamboa-Kroesen

Abstract:

School connectedness, or the sense of belonging at school, is a critical factor in adolescent health, academic achievement, and socioemotional well-being. In educational research, the construct of the psychological sense of school membership is often referred to as school engagement, school bonding, or school attachment. While current research recognizes school connectedness as integral to a child’s mental health and academic success, many schools have yet to develop adequate interventions to promote a child’s overall sense of belonging at school. However, prior researches in music education indicates that, among other benefits, music classrooms may provide an environment where students feel they belong. While studies indicates that music learning environments, specifically performing ensemble learning environments, instill a sense of school connectedness and, more broadly, contribute to a student’s socio-emotional development, there has been inadequate research on how the actions of music teachers contribute to this phenomenon. The purpose of this study was to examine the relationship between school connectedness and music learning environments with middle school music students enrolled in a school-based music ensemble. In addition, the study aimed to provide a descriptive analysis of the instructional practices that music teachers use to promote an inclusive environment in their classrooms and an overall sense of belonging in their students. Using 191 student surveys of school membership, student reflective writings, 5 teacher interviews, and 10 classroom observations, this study examined the relationship between 7th and 8th-grade student-reported levels of connectedness within their school-based music ensemble and teacher instructional practice. The study found that students reported high levels of positive school membership within their music classes. Students who participate in school-based orchestra ensembles reported a positive change in emotional state during music instruction. In addition, evidence in this study found that music teachers use instructional practices to build connectedness through de-emphasizing competition and strengthening a student’s sense of relational value within their music learning experience. The findings offer implications for future music teacher instruction to create environments of inclusion, strengthen student-teacher relationships, and promote strategies that enhance student connection to school.

Keywords: music education, belonging, instructional practice, school connectedness

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10176 Distributed Cyber Physical Secure Framework for DC Microgrids: DC Ship Power System Applications

Authors: Grace karimi Muriithi, Behnaz Papari, Ali Arsalan, Christopher Shannon Edrington

Abstract:

Complexity and nonlinearity of the control system design is increasing for DC microgrid applications when the cyber concept associated with the technology constraints will added to the picture. Controllers’ functionality during the critical operation mode is required to guaranteed specifically for a high profile applications such as NAVY DC ship power system (SPS) as an small-scaled DC microgrid. Thus, SPS is susceptible to cyber-attacks and, accordingly, can provide the disastrous effects. In this study, a machine learning (ML) approach is demonstrated to offer the promising performance of SPS for developing an effective and robust functionality over attacks time. Simulation results analysis demonstrate that the proposed method can improve the controllability successfully.

Keywords: controlability, cyber attacks, distribute control, machine learning

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10175 The Τraits Τhat Facilitate Successful Student Performance in Distance Education: The Case of the Distance Education Unit at European University Cyprus

Authors: Dimitrios Vlachopoulos, George Tsokkas

Abstract:

Although it is not intended to identify distance education students as a homogeneous group, recent research has demonstrated that there are some demographic and personality common traits among most of them that provide the basis for the description of a typical distance learning student. The purpose of this paper is to describe these common traits and to facilitate their learning journey within a distance education program. The described research is an initiative of the Distance Education Unit at the European University Cyprus (Laureate International Universities) in the context of its action for the improvement of the students’ performance.

Keywords: distance education students, successful student performance, European University Cyprus, common traits

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10174 Developing an AI-Driven Application for Real-Time Emotion Recognition from Human Vocal Patterns

Authors: Sayor Ajfar Aaron, Mushfiqur Rahman, Sajjat Hossain Abir, Ashif Newaz

Abstract:

This study delves into the development of an artificial intelligence application designed for real-time emotion recognition from human vocal patterns. Utilizing advanced machine learning algorithms, including deep learning and neural networks, the paper highlights both the technical challenges and potential opportunities in accurately interpreting emotional cues from speech. Key findings demonstrate the critical role of diverse training datasets and the impact of ambient noise on recognition accuracy, offering insights into future directions for improving robustness and applicability in real-world scenarios.

Keywords: artificial intelligence, convolutional neural network, emotion recognition, vocal patterns

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10173 Comparison Study of Machine Learning Classifiers for Speech Emotion Recognition

Authors: Aishwarya Ravindra Fursule, Shruti Kshirsagar

Abstract:

In the intersection of artificial intelligence and human-centered computing, this paper delves into speech emotion recognition (SER). It presents a comparative analysis of machine learning models such as K-Nearest Neighbors (KNN),logistic regression, support vector machines (SVM), decision trees, ensemble classifiers, and random forests, applied to SER. The research employs four datasets: Crema D, SAVEE, TESS, and RAVDESS. It focuses on extracting salient audio signal features like Zero Crossing Rate (ZCR), Chroma_stft, Mel Frequency Cepstral Coefficients (MFCC), root mean square (RMS) value, and MelSpectogram. These features are used to train and evaluate the models’ ability to recognize eight types of emotions from speech: happy, sad, neutral, angry, calm, disgust, fear, and surprise. Among the models, the Random Forest algorithm demonstrated superior performance, achieving approximately 79% accuracy. This suggests its suitability for SER within the parameters of this study. The research contributes to SER by showcasing the effectiveness of various machine learning algorithms and feature extraction techniques. The findings hold promise for the development of more precise emotion recognition systems in the future. This abstract provides a succinct overview of the paper’s content, methods, and results.

Keywords: comparison, ML classifiers, KNN, decision tree, SVM, random forest, logistic regression, ensemble classifiers

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10172 Cognition of Driving Context for Driving Assistance

Authors: Manolo Dulva Hina, Clement Thierry, Assia Soukane, Amar Ramdane-Cherif

Abstract:

In this paper, we presented our innovative way of determining the driving context for a driving assistance system. We invoke the fusion of all parameters that describe the context of the environment, the vehicle and the driver to obtain the driving context. We created a training set that stores driving situation patterns and from which the system consults to determine the driving situation. A machine-learning algorithm predicts the driving situation. The driving situation is an input to the fission process that yields the action that must be implemented when the driver needs to be informed or assisted from the given the driving situation. The action may be directed towards the driver, the vehicle or both. This is an ongoing work whose goal is to offer an alternative driving assistance system for safe driving, green driving and comfortable driving. Here, ontologies are used for knowledge representation.

Keywords: cognitive driving, intelligent transportation system, multimodal system, ontology, machine learning

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10171 Fabrication of Highly-Ordered Interconnected Porous Polymeric Particles and Structures

Authors: Mohammad Alroaithi

Abstract:

Porous polymeric materials have attracted a great attention due to their distinctive porous structure within a polymer matrix. They are characterised by the presence of external pores on the surface as well as inner interconnected windows. Conventional techniques to produce porous polymeric materials encounters major challenge in controlling the properties of the resultant structures including morphology, pores, cavities size, and porosity. Herein, we present a facile and versatile microfluidics technique for the fabrication of uniform porous polymeric structures with highly ordered and well-defined interconnected windows. The shapes of the porous structures can either be a microparticles or foam. Both shapes used microfluidics platform to first produce monodisperse emulsion. The uniform emulsions, were then consolidated into porous structures through UV photopolymerisation. The morphology, pores, cavities size, and porosity of the structures can be precisely manipulated by the flowrate. The proposed strategy might provide a key advantage for fabrication of uniform porous materials over many existing technologies.

Keywords: polymer, porous particles, microfluidics, porous structures

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10170 Supporting the ESL Student in a Tertiary Setting: Carrot and Stick

Authors: Ralph Barnes

Abstract:

The internationalization and globalization of education are now a huge, multi-million dollar industry. The movement of international students across the globe has provided a rich vein of revenue for universities and institutions of higher learning to exploit and harvest. A concerted effort has been made by universities worldwide to court students from overseas, with some countries relying up to one-third of student fees, coming from international students. Australian universities and English Language Centres are coming under increased government scrutiny in respect to such areas as the academic progression of international students, management and understanding of student visa requirements and the design of higher education courses and effective assessment regimes. As such, universities and other higher education institutions are restructuring themselves more as service providers rather than as strictly education providers. In this paper, the high-touch, tailored academic model currently followed by some Australian educational institutions to support international students, is examined and challenged. Academic support services offered to international students need to be coordinated, sustained and reviewed regularly, in order to assess their effectiveness. Maintaining the delivery of high-quality educational programs and learning outcomes for this high income-generating student cohort is vital, in order to continue the successful academic and social engagement by international students across the Australian university and higher education landscape.

Keywords: ESL, engagement, tertiary, learning

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